{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sys.version_info(major=3, minor=10, micro=14, releaselevel='final', serial=0)\n",
      "matplotlib 3.10.0\n",
      "numpy 1.26.4\n",
      "pandas 2.2.3\n",
      "sklearn 1.6.0\n",
      "torch 2.5.1+cu124\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import pandas as pd\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "print(sys.version_info)\n",
    "for module in mpl, np, pd, sklearn, torch:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "print(device)\n",
    "\n",
    "seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c29f1d6fe09a9955",
   "metadata": {},
   "source": [
    "# 数据加载\n",
    "\n",
    "- 采用WMT16的德语和英语平行语料库，数据集主页：[WMT16](https://www.statmt.org/wmt16/multimodal-task.html#task1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d047d1eed0336fbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:28.942222Z",
     "start_time": "2025-02-06T14:04:28.938639Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:31.173184Z",
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     "shell.execute_reply": "2025-02-07T10:26:31.175332Z",
     "shell.execute_reply.started": "2025-02-07T10:26:31.173148Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # 调用脚本文件\n",
    "# !pip install sacremoses\n",
    "# !sh data_multi30k.sh wmt16 wmt16_cut de en"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a930b5668e422750",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c0f0f4781f5e042f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.020273Z",
     "start_time": "2025-02-06T14:04:29.011246Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:31.177300Z",
     "iopub.status.busy": "2025-02-07T10:26:31.176803Z",
     "iopub.status.idle": "2025-02-07T10:26:31.185199Z",
     "shell.execute_reply": "2025-02-07T10:26:31.184429Z",
     "shell.execute_reply.started": "2025-02-07T10:26:31.177263Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class LangPairDataset(Dataset):\n",
    "    def __init__(self, mode=\"train\", max_length=128,\n",
    "                 overwrite_cache=False, data_dir=\"wmt16\"):\n",
    "        self.data_dir = Path(data_dir)\n",
    "        cache_path = self.data_dir / \".cache\" / f\"de2en_{mode}_{max_length}.npy\"\n",
    "\n",
    "        if overwrite_cache or not cache_path.exists():\n",
    "            # parents=True：如果父目录不存在，会自动创建所有必要的父目录。\n",
    "            # exist_ok=True：如果目录已经存在，不会抛出错误。\n",
    "            cache_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "            # 读取源语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_src.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.src = f.readlines()\n",
    "            # 读取目标语言文件所有行\n",
    "            with open(self.data_dir / f\"{mode}_trg.bpe\", \"r\", encoding=\"utf8\") as f:\n",
    "                self.trg = f.readlines()\n",
    "\n",
    "            filtered_src = []\n",
    "            filtered_trg = []\n",
    "            # max length filter,超出最大长度的句子舍弃\n",
    "            for src, trg in zip(self.src, self.trg):\n",
    "                # 过滤长度超过最大长度的句子\n",
    "                if len(src) <= max_length and len(trg) <= max_length:\n",
    "                    filtered_src.append(src.strip())  # 去掉句子前后的空格\n",
    "                    filtered_trg.append(trg.strip())\n",
    "            filtered_src = np.array(filtered_src)\n",
    "            filtered_trg = np.array(filtered_trg)\n",
    "            #allow_pickle=True允许保存对象数组，将过滤后的数据保存为 NumPy 数组，存储在缓存文件中\n",
    "            np.save(\n",
    "                cache_path,\n",
    "                {\"src\": filtered_src, \"trg\": filtered_trg},\n",
    "                allow_pickle=True\n",
    "            )\n",
    "            print(f\"save cache to {cache_path}\")\n",
    "\n",
    "        else:\n",
    "            # allow_pickle=True允许保存对象数组\n",
    "            cache_dict = np.load(cache_path, allow_pickle=True).item()\n",
    "            print(f\"load {mode} dataset from {cache_path}\")\n",
    "            filtered_src = cache_dict[\"src\"]\n",
    "            filtered_trg = cache_dict[\"trg\"]\n",
    "\n",
    "        self.src = filtered_src\n",
    "        self.trg = filtered_trg\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return self.src[index], self.trg[index]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.src)\n",
    "\n",
    "# train_ds = LangPairDataset(\"train\")\n",
    "# val_ds = LangPairDataset(\"val\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2a0f23be5871b84",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.097548Z",
     "start_time": "2025-02-06T14:04:29.094294Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:31.186394Z",
     "iopub.status.busy": "2025-02-07T10:26:31.186026Z",
     "iopub.status.idle": "2025-02-07T10:26:31.189222Z",
     "shell.execute_reply": "2025-02-07T10:26:31.188553Z",
     "shell.execute_reply.started": "2025-02-07T10:26:31.186363Z"
    }
   },
   "outputs": [],
   "source": [
    "# print(len(train_ds))\n",
    "# print(len(val_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eaf27846b953da4",
   "metadata": {},
   "source": [
    "## Tokenizer\n",
    "\n",
    "这里有两种处理方式，分别对应着 encoder 和 decoder 的 word embedding 是否共享，这里实现共享的方案"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "df6173e2f201c301",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.169362Z",
     "start_time": "2025-02-06T14:04:29.138854Z"
    },
    "execution": {
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     "shell.execute_reply": "2025-02-07T10:26:31.216038Z",
     "shell.execute_reply.started": "2025-02-07T10:26:31.190404Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 18107/18107 [00:00<00:00, 1093491.46it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocab_size: 18111\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "word2idx = {\n",
    "    \"[PAD]\": 0,  # 填充 token\n",
    "    \"[BOS]\": 1,  # begin of sentence\n",
    "    \"[UNK]\": 2,  # 未知 token\n",
    "    \"[EOS]\": 3,  # end of sentence\n",
    "}\n",
    "\n",
    "idx2word = {value: key for key, value in word2idx.items()}\n",
    "index = len(idx2word)\n",
    "threshold = 1  # 出现次数低于此的token舍弃\n",
    "\n",
    "with open(\"wmt16/vocab\", \"r\", encoding=\"utf8\") as fd:\n",
    "    for line in tqdm(fd.readlines()):\n",
    "        token, counts = line.strip().split()\n",
    "        if int(counts) >= threshold:\n",
    "            word2idx[token] = index\n",
    "            idx2word[index] = token\n",
    "            index += 1\n",
    "\n",
    "vocab_size = len(word2idx)\n",
    "print(\"vocab_size: {}\".format(vocab_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "461056213fba3ed4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.226412Z",
     "start_time": "2025-02-06T14:04:29.217164Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:31.218105Z",
     "iopub.status.busy": "2025-02-07T10:26:31.217763Z",
     "iopub.status.idle": "2025-02-07T10:26:31.227654Z",
     "shell.execute_reply": "2025-02-07T10:26:31.227088Z",
     "shell.execute_reply.started": "2025-02-07T10:26:31.218072Z"
    }
   },
   "outputs": [],
   "source": [
    "class Tokenizer:\n",
    "    def __init__(self, word2idx, idx2word, max_length=128,\n",
    "                 pad_idx=0, bos_idx=1, eos_idx=3, unk_idx=2):\n",
    "        self.word2idx = word2idx\n",
    "        self.idx2word = idx2word\n",
    "        self.max_length = max_length\n",
    "        self.pad_idx = pad_idx\n",
    "        self.bos_idx = bos_idx\n",
    "        self.eos_idx = eos_idx\n",
    "        self.unk_idx = unk_idx\n",
    "\n",
    "    def encode(self, text_list, padding_first=False, add_bos=True, add_eos=True,\n",
    "               return_mask=False):\n",
    "        # 如果padding_first == True，则padding加载前面，否则加载后面\n",
    "        # return_mask: 是否返回mask(掩码），mask用于指示哪些是padding的，哪些是真实的token \n",
    "        # 若启动bos_eos，则在句首和句尾分别添加bos和eos，则 add_bos=True, add_eos=True即都为1\n",
    "        max_length = min(self.max_length, add_bos + add_eos + max([len(text) for text in text_list]))\n",
    "        indices_list = []\n",
    "        for text in text_list:\n",
    "            # 如果词表中没有这个词，就用unk_idx代替，indices是一个list,里面是每个词的index,也就是一个样本的index\n",
    "            indices = [self.word2idx.get(word, self.unk_idx) for word in text[:max_length - add_eos - add_bos]]\n",
    "            if add_bos:\n",
    "                indices = [self.bos_idx] + indices\n",
    "            if add_eos:\n",
    "                indices = indices + [self.eos_idx]\n",
    "            if padding_first:  # padding加载前面，超参可以调\n",
    "                indices = [self.pad_idx] * (max_length - len(indices)) + indices\n",
    "            else:  # padding加载后面\n",
    "                indices = indices + [self.pad_idx] * (max_length - len(indices))\n",
    "            indices_list.append(indices)\n",
    "        input_ids = torch.tensor(indices_list)  # 转换为tensor\n",
    "        # mask是一个和input_ids一样大小的tensor，0代表token，1代表padding，mask用于去除padding的影响\n",
    "        masks = (input_ids == self.pad_idx).to(dtype=torch.float64)\n",
    "        return input_ids if not return_mask else (input_ids, masks)\n",
    "\n",
    "    def decode(self, indices_list, remove_bos=True, remove_eos=True, remove_pad=True, split=False):\n",
    "        text_list = []\n",
    "        for indices in indices_list:\n",
    "            text = []\n",
    "            for index in indices:\n",
    "                # 如果词表中没有这个词，就用unk_idx代替\n",
    "                word = self.idx2word.get(index, \"[UNK]\")\n",
    "                if remove_bos and word == \"[BOS]\":\n",
    "                    continue\n",
    "                if remove_eos and word == \"[EOS]\":  # 如果到达eos，就结束\n",
    "                    break\n",
    "                if remove_pad and word == \"[PAD]\":  # 如果到达pad，就结束\n",
    "                    break\n",
    "                text.append(word)  # 单词添加到列表中\n",
    "            # 把列表中的单词拼接，变为一个句子\n",
    "            text_list.append(\" \".join(text) if not split else text)\n",
    "        return text_list\n",
    "\n",
    "\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word)\n",
    "\n",
    "# tokenizer.encode([[\"hello\"], [\"hello\", \"world\"]], add_bos=True, add_eos=False)\n",
    "# raw_text = [\"hello world\".split(), \"tokenize text datas with batch\".split(), \"this is a test\".split()]\n",
    "# indices = tokenizer.encode(raw_text, padding_first=False, add_bos=True, add_eos=True)\n",
    "# decode_text = tokenizer.decode(indices.tolist(), remove_bos=False, remove_eos=False, remove_pad=False)\n",
    "# print(\"raw text\")\n",
    "# for raw in raw_text:\n",
    "#     print(raw)\n",
    "# print(\"indices\")\n",
    "# for index in indices:\n",
    "#     print(index)\n",
    "# print(\"decode text\")\n",
    "# for decode in decode_text:\n",
    "#     print(decode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "628dded826e86608",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.254457Z",
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     "iopub.status.busy": "2025-02-07T10:26:31.229438Z",
     "iopub.status.idle": "2025-02-07T10:26:31.232182Z",
     "shell.execute_reply": "2025-02-07T10:26:31.231599Z",
     "shell.execute_reply.started": "2025-02-07T10:26:31.229682Z"
    }
   },
   "outputs": [],
   "source": [
    "# for i,j in train_ds:\n",
    "#     print(len(i))\n",
    "#     print(len(j))\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43df9ce1d4860bb",
   "metadata": {},
   "source": [
    "## Transformer Batch Sampler\n",
    "\n",
    "> Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens\n",
    "句子按照序列长度差不多的分到一个批次。 每个训练批次包含一组句子对，其中包含大约 25000 个源标记和 25000 个目标标记"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7c072c0e714bd8ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.298858Z",
     "start_time": "2025-02-06T14:04:29.288864Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:31.232999Z",
     "iopub.status.busy": "2025-02-07T10:26:31.232773Z",
     "iopub.status.idle": "2025-02-07T10:26:31.239001Z",
     "shell.execute_reply": "2025-02-07T10:26:31.238445Z",
     "shell.execute_reply.started": "2025-02-07T10:26:31.232979Z"
    }
   },
   "outputs": [],
   "source": [
    "# 下面的info对象\n",
    "class SampleInfo:\n",
    "    def __init__(self, i, lens):\n",
    "        \"\"\"\n",
    "        记录文本对的序号和长度信息\n",
    "        输入：\n",
    "            - i (int): 文本对的序号。\n",
    "            - lens (list): 文本对源语言和目标语言的长度\n",
    "        \"\"\"\n",
    "        self.i = i\n",
    "        # 加一是考虑填补在文本前后的特殊词元，lens[0]和lens[1]分别表示源语言和目标语言的长度\n",
    "        self.max_len = max(lens[0], lens[1]) + 1\n",
    "        self.src_len = lens[0] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "        self.trg_len = lens[1] + 1  # 加一是考虑填补在文本前后的特殊词元\n",
    "\n",
    "\n",
    "# 一个批量生成器，根据词元数目的限制来控制批量的大小。\n",
    "# 它会根据传入的样本信息，在不超过设定大小的情况下，逐步构建批量。\n",
    "class TokenBatchCreator:\n",
    "    def __init__(self, batch_size):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        batch_size (int): 用于限制批量的大小。\n",
    "        功能:\n",
    "        初始化了一个空的批量列表 _batch。\n",
    "        设定了初始的最大长度为 -1。\n",
    "        存储了传入的 batch_size。\n",
    "        \"\"\"\n",
    "        self._batch = []  # 这个就是之前的batch_size，就是一个batch内有多少个样本\n",
    "        self.max_len = -1\n",
    "        self.batch_size = batch_size  # 限制批量的大小,假设是4096\n",
    "\n",
    "    def append(self, info: SampleInfo):\n",
    "        \"\"\"\n",
    "        参数:\n",
    "        info (SampleInfo): 文本对的信息。\n",
    "        功能:\n",
    "        接收一个 SampleInfo 对象，并根据其最大长度信息更新当前批量的最大长度。\n",
    "        如果将新的样本加入批量后超过了批量大小限制，它会返回已有的批量并将新的样本加入新的批量。\n",
    "        否则，它会更新最大长度并将样本添加到当前批量中。\n",
    "        \"\"\"\n",
    "        # 更新当前批量的最大长度\n",
    "        cur_len = info.max_len  # 当前样本的长度\n",
    "        max_len = max(self.max_len, cur_len)  # 每来一个样本，更新当前批次的最大长度\n",
    "\n",
    "        # 如果新的样本加入批量后超过大小限制，则将已有的批量返回，新的样本加入新的批量\n",
    "        # 同一批样本计算时，短的样本向最长的样本对齐，所以用max_len来计算\n",
    "        if max_len * (len(self._batch) + 1) > self.batch_size:\n",
    "            # result_batch保存原有的_batch，_batch清空\n",
    "            self._batch, result_batch = [], self._batch\n",
    "            self._batch.append(info)  #箱子里的第一条样本，放入\n",
    "            # 因为是当前batch的第一个样本，所以它的长度就是当前长度\n",
    "            self.max_len = cur_len\n",
    "            return result_batch\n",
    "        else:\n",
    "            self.max_len = max_len\n",
    "            self._batch.append(info)\n",
    "            return None\n",
    "\n",
    "    # 将类的方法转换为属性，从而实现对类属性的访问、修改和删除的控制\n",
    "    @property\n",
    "    def batch(self):\n",
    "        return self._batch"
   ]
  },
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   "id": "c1dd026c49a90114",
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   "source": [
    "from torch.utils.data import BatchSampler\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "class TransformerBatchSampler(BatchSampler):\n",
    "    def __init__(self, dataset, batch_size, shuffle_batch=False,\n",
    "                 clip_last_batch=False, seed=0):\n",
    "        \"\"\"\n",
    "        批量采样器\n",
    "        输入:\n",
    "            - dataset: 数据集\n",
    "            - batch_size: 批量大小\n",
    "            - shuffle_batch: 是否对生成的批量进行洗牌\n",
    "            - clip_last_batch: 是否裁剪最后剩下的数据\n",
    "            - seed: 随机数种子\n",
    "        \"\"\"\n",
    "        self._dataset = dataset\n",
    "        self._batch_size = batch_size\n",
    "        self._shuffle_batch = shuffle_batch\n",
    "        self._clip_last_batch = clip_last_batch\n",
    "        self._seed = seed\n",
    "        self._random = np.random\n",
    "        self._random.seed(seed)\n",
    "\n",
    "        self._sample_infos = []\n",
    "        # 根据数据集中的每个样本，创建了对应的 SampleInfo 对象，包含了样本的索引和长度信息\n",
    "        for i, data in enumerate(self._dataset):\n",
    "            lens = [len(data[0]), len(data[1])]  #输入和输出的长度计算放到lens中\n",
    "            self._sample_infos.append(SampleInfo(i, lens))\n",
    "\n",
    "    def __iter__(self):\n",
    "        \"\"\"\n",
    "        对数据集中的样本进行排序，排序规则是先按源语言长度排序，如果相同则按目标语言长度排序。\n",
    "        使用 TokenBatchCreator 逐步组装批量数据，当满足批量大小时返回一个批量的样本信息。\n",
    "        如果不裁剪最后一个批次的数据且存在剩余样本，则将这些样本组成最后一个批次。\n",
    "        如果需要对批量进行洗牌，则对批次进行洗牌操作。\n",
    "        通过迭代器，抛出每个批量的样本在数据集中的索引。\n",
    "        \"\"\"\n",
    "        # 排序，如果源语言长度相同则按照目标语言的长度排列\n",
    "        infos = sorted(self._sample_infos,\n",
    "                       key=lambda x: (x.src_len, x.trg_len))\n",
    "        # 组装批量，所有的batch都放入batch_infos\n",
    "        batch_infos = []\n",
    "        # 批量生成器\n",
    "        batch_creator = TokenBatchCreator(self._batch_size)\n",
    "        for info in infos:\n",
    "            batch = batch_creator.append(info)\n",
    "            # 存够一个batch的样本信息后，会把这个batch返回，否则返回为None\n",
    "            if batch is not None:\n",
    "                batch_infos.append(batch)\n",
    "\n",
    "        # 是否抛弃最后批量的文本对\n",
    "        # 如果最后一个batch的样本数目不足一个batch，则将其剩余的样本作为最后一个batch\n",
    "        if not self._clip_last_batch and len(batch_creator.batch) != 0:\n",
    "            batch_infos.append(batch_creator.batch)  # 最后一个batch\n",
    "\n",
    "        # 打乱batch\n",
    "        # 这里的shuffle是对batch_infos进行shuffle，而不是对batch_infos中的样本进行shuffle\n",
    "        if self._shuffle_batch:\n",
    "            self._random.shuffle(batch_infos)\n",
    "\n",
    "        self.batch_number = len(batch_infos)\n",
    "\n",
    "        # 抛出一个批量的文本对在数据集中的序号\n",
    "        for batch in batch_infos:\n",
    "            # info.i 是文本对的索引\n",
    "            batch_indices = [info.i for info in batch]\n",
    "            yield batch_indices\n",
    "\n",
    "    def __len__(self):\n",
    "        \"\"\"\n",
    "        返回批量的数量\n",
    "        \"\"\"\n",
    "        if hasattr(self, \"batch_number\"):\n",
    "            return self.batch_number\n",
    "        # 计算批量的数量,没有用到下面的情况，不用看\n",
    "        batch_number = (len(self._dataset) +\n",
    "                        self._batch_size) // self._batch_size\n",
    "        return batch_number\n"
   ]
  },
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   "cell_type": "code",
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   "id": "71ae51fb9433db98",
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   "outputs": [],
   "source": [
    "# sampler = TransformerBatchSampler(train_ds, batch_size=4096, shuffle_batch=True)\n",
    "# \n",
    "# #为什么这里每个批量的样本对数目不一样呢？长度*batch_number>4096的时候，就会返回上一个batch，然后新的样本加入新的batch,具体要看TokenBatchCreator的40行\n",
    "# for idx, batch in enumerate(sampler):\n",
    "#     print(\"第{}批量的数据中含有文本对是：{}，数量为：{}\".format(idx, batch, len(batch)))\n",
    "#     if idx >= 3:\n",
    "#         break\n",
    "# \n",
    "# print(\"-\"*50)\n",
    "# print(len(sampler))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b1add788035b2a",
   "metadata": {},
   "source": [
    "## DataLoader"
   ]
  },
  {
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   "execution_count": 11,
   "id": "24e99727ef49605",
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   "source": [
    "def collate_fn(batch, tokenizer):\n",
    "    # 取batch内第0列进行分词，赋给src_words\n",
    "    src_words = [pair[0].split() for pair in batch]\n",
    "    # 取batch内第1列进行分词，赋给trg_words\n",
    "    trg_words = [pair[1].split() for pair in batch]\n",
    "\n",
    "    # paddings 在前在后影响不大，但在后好理解，所以padding_first=False\n",
    "    # [BOS] src [EOS] [PAD] \n",
    "    encoder_inputs, encoder_inputs_mask = tokenizer.encode(\n",
    "        src_words, padding_first=False, add_bos=True, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    # 目标语言 [BOS] trg [PAD]\n",
    "    decoder_inputs = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=True, add_eos=False, return_mask=False\n",
    "    )\n",
    "\n",
    "    # 用于后续计算loss\n",
    "    # trg [EOS] [PAD]\n",
    "    decoder_labels, decoder_labels_mask = tokenizer.encode(\n",
    "        trg_words, padding_first=False, add_bos=False, add_eos=True, return_mask=True\n",
    "    )\n",
    "\n",
    "    return {\n",
    "        \"encoder_inputs\": encoder_inputs.to(device),\n",
    "        \"encoder_inputs_mask\": encoder_inputs_mask.to(device),\n",
    "        \"decoder_inputs\": decoder_inputs.to(device),\n",
    "        \"decoder_labels\": decoder_labels.to(device),\n",
    "        \"decoder_labels_mask\": decoder_labels_mask.to(device),\n",
    "    }  # 当返回的数据较多时，用dict返回比较合理\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f53f13a987ea9201",
   "metadata": {
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   "source": [
    "from functools import partial  # 固定collate_fct的tokenizer参数\n",
    "\n",
    "# # 可以调大batch_size,来看最终的bleu，如果GPU内存不够，可以减小batch_size\n",
    "# sampler = TransformerBatchSampler(train_ds, batch_size=256, shuffle_batch=True)\n",
    "# \n",
    "# # batch_sampler 是一个自定义的批次采样器，用于控制如何从数据集中采样批次。与 batch_size 和 shuffle 不同，batch_sampler 可以更灵活地定义批次的组织方式（如按长度分组）。\n",
    "# # partial(collate_fn, tokenizer=tokenizer) 使用 functools.partial 将 tokenizer 参数固定到 collate_fn 中，生成一个新的函数。\n",
    "# sample_dl = DataLoader(train_ds, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "# \n",
    "# for batch in sample_dl:  #外层是拿每个batch\n",
    "#     for key, value in batch.items():  #内层是拿每个batch里面是一个字典\n",
    "#         print(key)\n",
    "#         print(\"-\" * 50)\n",
    "#         print(value)\n",
    "#         print(\"-\" * 50)\n",
    "#     break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa4932b257d82059",
   "metadata": {},
   "source": [
    "# 定义模型\n",
    "\n",
    "- Transformer模型由Embedding、Transformer-Block组成\n",
    "- Embedding包括：\n",
    "    - WordEmbedding\n",
    "    - PositionEmbedding\n",
    "- Transformer-Block包括：\n",
    "    - Self-Attention\n",
    "    - Cross-Attention\n",
    "    - MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d2988138abac64d",
   "metadata": {},
   "source": [
    "## Embedding"
   ]
  },
  {
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    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class TransformerEmbedding(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.hidden_size = config[\"d_model\"]  # 词向量维度\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "\n",
    "        # layers,设置padding_idx可以让pad的词向量全为0\n",
    "        self.word_embedding = nn.Embedding(\n",
    "            self.vocab_size, self.hidden_size, padding_idx=self.pad_idx\n",
    "        )\n",
    "        self.position_embedding = nn.Embedding(\n",
    "            self.max_length, self.hidden_size,\n",
    "            # 位置编码，权重通过get_positional_encoding函数计算得到\n",
    "            _weight=self.get_position_encoding(self.max_length, self.hidden_size)\n",
    "        )\n",
    "\n",
    "        self.position_embedding.weight.requires_grad_(False)  # 不更新位置编码的权重\n",
    "        self.dropout = nn.Dropout(dropout_rate)  # 随机失活层\n",
    "\n",
    "    def get_word_embedding_weight(self):\n",
    "        return self.word_embedding.weight\n",
    "\n",
    "    # 计算位置信息\n",
    "    # 用于定义类方法（class method）。\n",
    "    # 类方法是绑定到类而不是实例的方法，可以通过类本身或类的实例调用。\n",
    "    @classmethod\n",
    "    def get_position_encoding(self, max_length, hidden_size):\n",
    "        # max_length是最大长度，hidden_size是embedding维度相等\n",
    "        pe = torch.zeros(max_length, hidden_size)  # 初始化位置编码\n",
    "        # .unsqueeze(1) 是将这个一维张量转换为二维张量，\n",
    "        # 即将其形状从 (max_length,) 变为 (max_length, 1)。\n",
    "        # 这个操作在张量的维度上增加了一个维度，使其从一维变为二维，第二维的大小为 1。\n",
    "        position = torch.arange(0, max_length).unsqueeze(1)  # 位置信息,从0到max_length-1\n",
    "        # 计算位置编码的权重,为了性能考量（是数学上的对数函数分解），这里用了log函数\n",
    "        # torch.arange(0, hidden_size, 2) 在 PyTorch 中用于创建一个一维张量，包含从 0 开始到（但不包括）hidden_size 的数值，步长为 2\n",
    "        div_term = torch.exp(\n",
    "            torch.arange(0, hidden_size, 2) *\n",
    "            -(torch.log(torch.Tensor([10000.0])) / hidden_size)\n",
    "        )\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)  # 偶数位置\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)  # 奇数位置\n",
    "        return pe\n",
    "\n",
    "    def forward(self, input_ids):\n",
    "        # input_ids: [batch_size,seq_len]\n",
    "        seq_len = input_ids.shape[1]\n",
    "        assert (\n",
    "                seq_len <= self.max_length), f\"input sequence length should no more than {self.max_length} but got {seq_len}\"\n",
    "\n",
    "        position_ids = torch.arange(seq_len, dtype=torch.long, device=input_ids.device)  # 位置信息\n",
    "        # unsqueeze(0): 在第一维添加一个维度，使张量的形状从 [seq_len] 变为 [1, seq_len]\n",
    "        # expand_as(input_ids): 扩展张量的形状，使其与 input_ids 张量的形状相同\n",
    "        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "\n",
    "        # print(input_ids.shape) #为了调试\n",
    "        # print(position_ids.shape) #为了调试\n",
    "        # print(position_ids) #为了调试\n",
    "\n",
    "        # embedding层\n",
    "        word_embeds = self.word_embedding(input_ids)  # 词嵌入\n",
    "        # word_embeds: [batch_size,seq_len,hidden_size]\n",
    "        # position_ids: [batch_size,seq_len]\n",
    "        pos_embeds = self.position_embedding(position_ids)  # 位置嵌入 # ？\n",
    "        # pos_embeds: [batch_size,seq_len,hidden_size]   \n",
    "        embeds = word_embeds + pos_embeds  # 相加得到嵌入向量\n",
    "        # embeds: [batch_size,seq_len,hidden_size]\n",
    "        embeds = self.dropout(embeds)  # 随机失活\n",
    "        return embeds\n",
    "\n",
    "\n",
    "# #随机input，调用TransformerEmbedding\n",
    "# config={\n",
    "#     \"vocab_size\": 100,\n",
    "#     \"d_model\": 128,\n",
    "#     \"pad_idx\": 0,\n",
    "#     \"max_length\": 64,\n",
    "#     \"dropout\": 0.1,\n",
    "# }\n",
    "# input_ids = torch.randint(0, 100, (2, 50))\n",
    "# embeds = TransformerEmbedding(config)(input_ids)\n",
    "# embeds.shape\n",
    "\n",
    "# 绘制位置编码\n",
    "def plot_position_embedding(position_embedding):\n",
    "    plt.pcolormesh(position_embedding)  # 绘制位置编码矩阵\n",
    "    plt.xlabel('Depth')\n",
    "    plt.ylabel('Position')\n",
    "    plt.colorbar()  # 颜色条，-1到1的颜色范围\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "position_embedding = TransformerEmbedding.get_position_encoding(64, 128)\n",
    "plot_position_embedding(position_embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39630e37b910bd7f",
   "metadata": {},
   "source": [
    "## Transformer Block"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e7edad4645f763",
   "metadata": {},
   "source": [
    "### scaled-dot-product-attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bd08ede5d628f45",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.834753Z",
     "start_time": "2025-02-06T14:04:29.821751Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:31.418855Z",
     "iopub.status.busy": "2025-02-07T10:26:31.418493Z",
     "iopub.status.idle": "2025-02-07T10:26:31.614122Z",
     "shell.execute_reply": "2025-02-07T10:26:31.613399Z",
     "shell.execute_reply.started": "2025-02-07T10:26:31.418835Z"
    }
   },
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "from typing import Optional, Tuple\n",
    "\n",
    "Tensor = torch.Tensor\n",
    "\n",
    "\n",
    "# 修饰器 @dataclass 会自动生成 __init__、__repr__ 和 __eq__ 等方法，减少了样板代码的编写\n",
    "@dataclass\n",
    "class AttentionOutput:\n",
    "    hidden_states: Tensor\n",
    "    attn_scores: Tensor\n",
    "\n",
    "\n",
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        assert (\n",
    "                self.hidden_size % self.num_heads == 0\n",
    "        ), \"Hidden size must be divisible by num_heads but got {} and {}\".format(\n",
    "            self.hidden_size, self.num_heads)\n",
    "        self.head_dim = self.hidden_size // self.num_heads  # 每个头的维度\n",
    "\n",
    "        # layers\n",
    "        # 第二个self.hidden_size可以*系数\n",
    "        self.Wq = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wk = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wv = nn.Linear(self.hidden_size, self.hidden_size, bias=False)\n",
    "        self.Wo = nn.Linear(self.hidden_size, self.hidden_size, bias=False)  # 输出层\n",
    "\n",
    "    # -> Tensor 是一种类型注解，表示函数的返回值类型是 Tensor\n",
    "    def _split_heads(self, x: Tensor) -> Tensor:\n",
    "        # 若hidden_size是512\n",
    "        bs, seq_len, _ = x.shape  # [batch_size,seq_len,hidden_size]\n",
    "        # 先将x变形为[batch_size,seq_len,num_heads,head_dim]\n",
    "        x = x.view(bs, seq_len, self.num_heads, self.head_dim)\n",
    "        # num_heads是8，head_dim是64\n",
    "        return x.permute(0, 2, 1, 3)\n",
    "        # 变换维度，[batch_size, num_heads, seq_len, head_dim]\n",
    "\n",
    "    def _merge_heads(self, x: Tensor) -> Tensor:\n",
    "        # 将多头注意力的输出合并为一个张量\n",
    "        bs, _, seq_len, _ = x.shape  # [batch_size, num_heads, seq_len, head_dim]\n",
    "        # 变换维度，变为[batch_size, seq_len, hidden_size]\n",
    "        return x.permute(0, 2, 1, 3).reshape(bs, seq_len, self.hidden_size)\n",
    "\n",
    "    def forward(self, querys, keys, values, attn_mask=None) -> AttentionOutput:\n",
    "        # split heads\n",
    "        # [batch_size, seq_len,hidden_dim]->[batch_size, num_heads, seq_len, head_dim]\n",
    "        querys = self._split_heads(self.Wq(querys))\n",
    "        keys = self._split_heads(self.Wk(keys))\n",
    "        values = self._split_heads(self.Wv(values))\n",
    "\n",
    "        # calculate attention scores\n",
    "        # 计算注意力分数，matmul是矩阵乘法(在后两个维度上进行)，mT是矩阵转置,\n",
    "        # qk_logits是[batch_size, num_heads, seq_len, seq_len]\n",
    "        qk_logits = torch.matmul(querys, keys.mT)\n",
    "        if attn_mask is not None:\n",
    "            # seq_len*seq_len的部分是有效的\n",
    "            attn_mask = attn_mask[:, :, :querys.shape[-2], :keys.shape[-2]]  #切片\n",
    "            qk_logits += attn_mask * -1e9  # 给需要mask的地方设置一个负无穷\n",
    "        # 计算注意力数,softmax是在seq_len维度上进行的\n",
    "        attn_scores = F.softmax(qk_logits / (self.head_dim ** 0.5), dim=-1)\n",
    "        # attn_scores: [batch_size, num_heads, seq_len, seq_len]\n",
    "\n",
    "        # softmax后的结果与value相乘，得到新的表示\n",
    "        embeds = torch.matmul(attn_scores, values)\n",
    "        # embeds的尺寸是[batch_size, num_heads, seq_len, head_dim]\n",
    "        # _merge_heads(embeds)的输出是[batch_size, seq_len, hidden_size]\n",
    "        embeds = self.Wo(self._merge_heads(embeds))  # 输出层\n",
    "        # embeds: [batch_size, seq_len, hidden_size]\n",
    "\n",
    "        return AttentionOutput(hidden_states=embeds, attn_scores=attn_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9eb177f33d1050d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.843757Z",
     "start_time": "2025-02-06T14:04:29.835755Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:31.615249Z",
     "iopub.status.busy": "2025-02-07T10:26:31.614945Z",
     "iopub.status.idle": "2025-02-07T10:26:31.622973Z",
     "shell.execute_reply": "2025-02-07T10:26:31.622321Z",
     "shell.execute_reply.started": "2025-02-07T10:26:31.615227Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key_value.shape torch.Size([2, 4, 2])\n",
      "torch.Size([2, 3, 2])\n",
      "torch.Size([2, 2, 3, 4])\n"
     ]
    }
   ],
   "source": [
    "mha = MultiHeadAttention({\"num_heads\": 2, \"d_model\": 2})\n",
    "query = torch.randn(2, 3, 2)  # [batch_size, seq_len, hidden_size]\n",
    "query /= query.norm(dim=-1, keepdim=True)  # 归一化\n",
    "key_value = torch.randn(2, 4, 2)\n",
    "print(f'key_value.shape {key_value.shape}')\n",
    "outputs = mha(query, key_value, key_value)  #最终输出shape和query的shape一样\n",
    "print(outputs.hidden_states.shape)\n",
    "print(outputs.attn_scores.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e37f29d98cf51bac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:29.852319Z",
     "start_time": "2025-02-06T14:04:29.845757Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:31.624038Z",
     "iopub.status.busy": "2025-02-07T10:26:31.623737Z",
     "iopub.status.idle": "2025-02-07T10:26:31.630794Z",
     "shell.execute_reply": "2025-02-07T10:26:31.630159Z",
     "shell.execute_reply.started": "2025-02-07T10:26:31.624016Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.3227, 0.1947, 0.4825],\n",
      "        [0.0981, 0.4996, 0.4024]])\n"
     ]
    }
   ],
   "source": [
    "#随机一个2阶张量，在-1维度计算softmax\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = torch.randn(2, 3)\n",
    "x_softmax = F.softmax(x, dim=-1)\n",
    "print(x_softmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b451e28eb10aaf26",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.232694Z",
     "start_time": "2025-02-06T14:04:30.002057Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:31.632247Z",
     "iopub.status.busy": "2025-02-07T10:26:31.631666Z",
     "iopub.status.idle": "2025-02-07T10:26:31.871046Z",
     "shell.execute_reply": "2025-02-07T10:26:31.870098Z",
     "shell.execute_reply.started": "2025-02-07T10:26:31.632205Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.subplots() 用于创建子图网格，其维度基于 outputs.attn_scores.shape[:2]。子图的行数和列数似乎由 outputs.attn_scores 的前两个维度确定。\n",
    "fig, axis = plt.subplots(*outputs.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs.attn_scores.shape[1]):\n",
    "        # axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())：此行使用 Matplotlib 的 matshow 绘制每个 i 和 j 的注意力分数热图。detach().numpy() 将 PyTorch 张量转换为 NumPy 数组以进行可视化。\n",
    "        axis[i, j].matshow(outputs.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs.attn_scores.shape[2]):\n",
    "            for y in range(outputs.attn_scores.shape[3]):\n",
    "                # axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")：此代码在热图上叠加文本，显示 (x, y) 位置处的注意力分数。格式化部分 f\"{outputs.attn_scores[i, j, x, y]:.2f}\" 确保以两位小数显示注意力分数。文本以白色居中显示在 (y, x) 坐标处。\n",
    "                axis[i, j].text(y, x, f\"{outputs.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\", color=\"w\")\n",
    "fig.suptitle(\"multi head attention without mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42dc1608bbcd426c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.480546Z",
     "start_time": "2025-02-06T14:04:30.233690Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:31.872690Z",
     "iopub.status.busy": "2025-02-07T10:26:31.872230Z",
     "iopub.status.idle": "2025-02-07T10:26:32.117375Z",
     "shell.execute_reply": "2025-02-07T10:26:32.116520Z",
     "shell.execute_reply.started": "2025-02-07T10:26:31.872650Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask\n",
    "mask = torch.Tensor([[0, 0, 1, 1], [0, 0, 0, 1], [0, 0, 0, 0]]).reshape(1, 1, 3, 4)  #手工构造mask\n",
    "outputs_masked = mha(query, key_value, key_value, mask)\n",
    "\n",
    "fig, axis = plt.subplots(*outputs_masked.attn_scores.shape[:2])\n",
    "for i in range(query.shape[0]):\n",
    "    for j in range(outputs_masked.attn_scores.shape[1]):\n",
    "        axis[i, j].matshow(outputs_masked.attn_scores[i, j].detach().numpy())\n",
    "        for x in range(outputs_masked.attn_scores.shape[2]):\n",
    "            for y in range(outputs_masked.attn_scores.shape[3]):\n",
    "                axis[i, j].text(y, x, f\"{outputs_masked.attn_scores[i, j, x, y]:.2f}\", ha=\"center\", va=\"center\",\n",
    "                                color=\"w\")\n",
    "fig.suptitle(\"multi head attention with mask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd2b7a6a479f877c",
   "metadata": {},
   "source": [
    "### Transformer-Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2247e4861550df31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.489814Z",
     "start_time": "2025-02-06T14:04:30.481541Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:32.118949Z",
     "iopub.status.busy": "2025-02-07T10:26:32.118506Z",
     "iopub.status.idle": "2025-02-07T10:26:32.132603Z",
     "shell.execute_reply": "2025-02-07T10:26:32.131711Z",
     "shell.execute_reply.started": "2025-02-07T10:26:32.118916Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerBlockOutput:\n",
    "    # 用于存储某个块产生的隐藏状态。\n",
    "    hidden_states: Tensor\n",
    "    # 包含了自注意力机制（self-attention）所计算得到的注意力分数\n",
    "    self_attn_scores: Tensor\n",
    "    # 是一个可选字段，存储了交叉注意力（cross-attention）计算得到的注意力分数。\n",
    "    # 这里的 Optional 表示这个字段可以是 Tensor 类型，也可以是 None。\n",
    "    cross_attn_scores: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, config, add_cross_attention=False):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]  # 隐藏层大小\n",
    "        self.num_heads = config[\"num_heads\"]  # 多头注意力的头数\n",
    "        dropout_rate = config[\"dropout\"]  # 随机失活率\n",
    "        ffn_dim = config[\"dim_feedforward\"]  # 前馈网络的维度\n",
    "        eps = config[\"layer_norm_eps\"]  # 层归一化的epsilon值\n",
    "\n",
    "        # self-attention\n",
    "        self.self_attn = MultiHeadAttention(config)  # 多头注意力\n",
    "        self.self_ln = nn.LayerNorm(self.hidden_size, eps=eps)  # 层归一化(层标准化)\n",
    "        self.self_dropout = nn.Dropout(dropout_rate)  # 随机失活\n",
    "\n",
    "        # cross-attention，交叉注意力，decoder中使用,因此额外做一个判断\n",
    "        if add_cross_attention:\n",
    "            self.cross_attn = MultiHeadAttention(config)\n",
    "            self.cross_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "            self.cross_dropout = nn.Dropout(dropout_rate)\n",
    "        else:\n",
    "            self.cross_attn = None\n",
    "\n",
    "        # FFN,前馈神经网络\n",
    "        self.ffn = nn.Sequential(\n",
    "            nn.Linear(self.hidden_size, ffn_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(ffn_dim, self.hidden_size),\n",
    "        )\n",
    "        self.ffn_ln = nn.LayerNorm(self.hidden_size, eps=eps)\n",
    "        self.ffn_dropout = nn.Dropout(dropout_rate)\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            hidden_states,\n",
    "            attn_mask=None,\n",
    "            encoder_outputs=None,\n",
    "            cross_attn_mask=None,\n",
    "    ):\n",
    "        # self-attention,自注意力\n",
    "        self_attn_outputs = self.self_attn(\n",
    "            hidden_states, hidden_states, hidden_states, attn_mask\n",
    "        )  # self_attn_outputs 是 AttentionOutput 类型\n",
    "        self_embeds = self.self_ln(\n",
    "            hidden_states + self.self_dropout(self_attn_outputs.hidden_states)\n",
    "        )  #多头注意力进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "\n",
    "        # cross-attention，交叉注意力\n",
    "        if self.cross_attn is not None:\n",
    "            assert encoder_outputs is not None, \"encoder_outputs should not be None\"\n",
    "            cross_attn_outputs = self.cross_attn(\n",
    "                self_embeds, encoder_outputs, encoder_outputs, cross_attn_mask\n",
    "            )  # query是self_embeds，key和value都是encoder_outputs\n",
    "            cross_embeds = self.cross_ln(\n",
    "                self_embeds + self.cross_dropout(cross_attn_outputs.hidden_states)\n",
    "            )  # 交叉注意力进行dropout，然后和self_embeds进行残差连接，然后进行层归一化\n",
    "\n",
    "        # FFN\n",
    "        # 如果有交叉注意力，则使用交叉注意力的输出作为FFN的输入；否则，使用self_embeds作为FFN的输入\n",
    "        embeds = cross_embeds if self.cross_attn is not None else self_embeds\n",
    "        # embeds:[batch_size, seq_len, hidden_size]\n",
    "        ffn_output = self.ffn(embeds)  # 前馈神经网络\n",
    "        # ffn_output:[batch_size, seq_len, hidden_size]\n",
    "        # 前馈神经网络进行dropout，然后和原始输入进行残差连接，然后进行层归一化\n",
    "        embeds = self.ffn_ln(embeds + self.ffn_dropout(ffn_output))\n",
    "\n",
    "        return TransformerBlockOutput(\n",
    "            hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_outputs.attn_scores,\n",
    "            cross_attn_scores=cross_attn_outputs.attn_scores if self.cross_attn is not None else None,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19be62a364cb5cc2",
   "metadata": {},
   "source": [
    "## Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f0e09edb3e7bbfe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.497908Z",
     "start_time": "2025-02-06T14:04:30.490811Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:32.133971Z",
     "iopub.status.busy": "2025-02-07T10:26:32.133656Z",
     "iopub.status.idle": "2025-02-07T10:26:32.140716Z",
     "shell.execute_reply": "2025-02-07T10:26:32.139900Z",
     "shell.execute_reply.started": "2025-02-07T10:26:32.133948Z"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class TransformerEncoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerEncoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_encoder_layers\"]\n",
    "\n",
    "        # layers,仅仅是一个模块的列表，它本身没有定义前向传递（forward pass）过程。你需要在 forward 方法中明确地定义如何使用这些模块。\n",
    "        self.layers = nn.ModuleList(\n",
    "            [TransformerBlock(config) for _ in range(self.num_layers)]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self, encoder_inputs_embeds, attn_mask=None\n",
    "    ) -> TransformerEncoderOutput:\n",
    "        # 存储每个层的注意力分数\n",
    "        attn_scores = []\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = encoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config)\n",
    "            block_outputs = layer(embeds, attn_mask=attn_mask)\n",
    "            # 在每个层的输出中，提取了隐藏状态 block_outputs.hidden_states，\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            # 并将对应的注意力分数 block_outputs.self_attn_scores 添加到列表 attn_scores 中。\n",
    "            attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的注意力分数,用于画图\n",
    "\n",
    "        return TransformerEncoderOutput(\n",
    "            last_hidden_states=embeds, attn_scores=attn_scores\n",
    "        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a142b31a53d27997",
   "metadata": {},
   "source": [
    "## Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0e7f64561cbd5f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.505801Z",
     "start_time": "2025-02-06T14:04:30.498905Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:32.141926Z",
     "iopub.status.busy": "2025-02-07T10:26:32.141631Z",
     "iopub.status.idle": "2025-02-07T10:26:32.149131Z",
     "shell.execute_reply": "2025-02-07T10:26:32.148399Z",
     "shell.execute_reply.started": "2025-02-07T10:26:32.141903Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerDecoderOutput:\n",
    "    last_hidden_states: Tensor\n",
    "    self_attn_scores: List[Tensor]\n",
    "    cross_attn_scores: List[Tensor]\n",
    "\n",
    "\n",
    "class TransformerDecoder(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.num_layers = config[\"num_decoder_layers\"]\n",
    "\n",
    "        # layers\n",
    "        self.layers = nn.ModuleList(\n",
    "            [\n",
    "                TransformerBlock(config, add_cross_attention=True)\n",
    "                for _ in range(self.num_layers)\n",
    "            ]\n",
    "        )\n",
    "\n",
    "    def forward(\n",
    "            self,\n",
    "            decoder_inputs_embeds,\n",
    "            encoder_outputs,\n",
    "            attn_mask=None,\n",
    "            cross_attn_mask=None,\n",
    "    ) -> TransformerDecoderOutput:\n",
    "        self_attn_scores = []  # 存储每个层的自注意力分数\n",
    "        cross_attn_scores = []  # 存储每个层的交叉注意力分数\n",
    "        # 输入的嵌入向量作为第一层的输入(embedding+位置编码)\n",
    "        embeds = decoder_inputs_embeds\n",
    "        for layer in self.layers:\n",
    "            # layer就是一个TransformerBlock(config, add_cross_attention=True)\n",
    "            # 为什么交叉注意力的mask要传入cross_attn_mask而不是attn_mask？\n",
    "            # 因为计算MutilHeadAttention的时候,keys和values都是encoder_outputs，query是decoder的输出，因此需要传入cross_attn_mask。\n",
    "            block_outputs = layer(\n",
    "                embeds,\n",
    "                attn_mask=attn_mask,  # 自注意力的mask\n",
    "                encoder_outputs=encoder_outputs,\n",
    "                cross_attn_mask=cross_attn_mask,  # 交叉注意力的mask\n",
    "            )\n",
    "            embeds = block_outputs.hidden_states  # 上一层的输出作为下一层的输入\n",
    "            self_attn_scores.append(block_outputs.self_attn_scores)  # 存储每个层的自注意力分数\n",
    "            cross_attn_scores.append(block_outputs.cross_attn_scores)  # 存储每个层的交叉注意力分数\n",
    "\n",
    "        return TransformerDecoderOutput(\n",
    "            last_hidden_states=embeds,\n",
    "            self_attn_scores=self_attn_scores,\n",
    "            cross_attn_scores=cross_attn_scores,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c345d9c0e326a13",
   "metadata": {},
   "source": [
    "#### mask\n",
    "\n",
    "- mask实际上大类上只有两种\n",
    "    1. `padding_mask`：mask掉`pad_idx`，不计算损失\n",
    "    2. `attention_mask`：mask掉`pad_idx`，不计算注意力分数\n",
    "- Decoder的`attention_mask`和Encoder有一定的区别：\n",
    "    - Encoder可以同时看见序列所有信息，故只mask掉`pad_idx`\n",
    "    - Decoder只能看到在自身之前的序列的信息，故要额外mask掉自身之后的序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9baa438dbfbcdd8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.512835Z",
     "start_time": "2025-02-06T14:04:30.506798Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:32.152654Z",
     "iopub.status.busy": "2025-02-07T10:26:32.152301Z",
     "iopub.status.idle": "2025-02-07T10:26:32.158866Z",
     "shell.execute_reply": "2025-02-07T10:26:32.158160Z",
     "shell.execute_reply.started": "2025-02-07T10:26:32.152631Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[False, False, False, False, False],\n",
      "        [ True, False, False, False, False],\n",
      "        [ True,  True, False, False, False],\n",
      "        [ True,  True,  True, False, False],\n",
      "        [ True,  True,  True,  True, False]])\n",
      "--------------------------------------------------\n",
      "tensor([[False,  True,  True,  True,  True],\n",
      "        [False, False,  True,  True,  True],\n",
      "        [False, False, False,  True,  True],\n",
      "        [False, False, False, False,  True],\n",
      "        [False, False, False, False, False]])\n"
     ]
    }
   ],
   "source": [
    "print((torch.triu(torch.ones(5, 5)) == 0))\n",
    "print(\"-\" * 50)\n",
    "# 符合Decoder的attention_mask形式\n",
    "print((torch.triu(torch.ones(5, 5)) == 0).transpose(-1, -2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52343e686b4eed69",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:30.667599Z",
     "start_time": "2025-02-06T14:04:30.513830Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:32.160132Z",
     "iopub.status.busy": "2025-02-07T10:26:32.159839Z",
     "iopub.status.idle": "2025-02-07T10:26:32.315579Z",
     "shell.execute_reply": "2025-02-07T10:26:32.314860Z",
     "shell.execute_reply.started": "2025-02-07T10:26:32.160106Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_square_subsequent_mask(sz: int) -> Tensor:\n",
    "    # The masked positions are filled with True.\n",
    "    # Unmasked positions are filled with False.\n",
    "    # torch.ones(sz, sz): 创建一个全为 1 的 sz × sz 的矩阵。\n",
    "    # torch.triu(...): 使用 triu 函数取得矩阵的上三角部分，将主对角线以下部分置零。\n",
    "    mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "    return mask\n",
    "\n",
    "\n",
    "#画出一个16×16的矩阵热力图，黄色部分为True，是掩码\n",
    "plt.matshow(generate_square_subsequent_mask(16))\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"keys\")\n",
    "plt.ylabel(\"querys\")\n",
    "plt.title(\"1 means mask while 0 means unmask\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2548d49c0f7a8fd5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.199068Z",
     "start_time": "2025-02-06T14:04:30.668599Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:32.316757Z",
     "iopub.status.busy": "2025-02-07T10:26:32.316419Z",
     "iopub.status.idle": "2025-02-07T10:26:32.810351Z",
     "shell.execute_reply": "2025-02-07T10:26:32.809691Z",
     "shell.execute_reply.started": "2025-02-07T10:26:32.316735Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['[BOS]', '[UNK]', 'quick', 'brown', '[UNK]', 'jumps', 'over', 'the', '[UNK]', 'dog', '.', '[EOS]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "['[BOS]', '[UNK]', 'does', 'the', '[UNK]', 'say', '?', '[EOS]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n",
      "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
      "        [0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1.]], dtype=torch.float64)\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#通过下面代码查看mask的效果\n",
    "inputs_words = [\"The quick brown fox jumps over the lazy dog .\", \"What does the fox say ?\"]\n",
    "\n",
    "inputs_ids, inputs_mask = tokenizer.encode([w.split() for w in inputs_words], return_mask=True)\n",
    "for i in range(len(inputs_ids)):\n",
    "    decode_text = \\\n",
    "        tokenizer.decode(inputs_ids[i:i + 1].tolist(), remove_bos=False, remove_eos=False, remove_pad=False,\n",
    "                         split=True)[0]\n",
    "    print(decode_text)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    print(inputs_mask[i].reshape(1, -1))\n",
    "    print(\"-\" * 50)\n",
    "    # reshape(1, -1):将 input_mask[i] 的形状调整为 (1, sequence_length)\n",
    "    # repeat_interleave 在第 0 维（dim=0）上重复 sequence_length 次，生成形状为 (sequence_length, sequence_length) 的掩码。\n",
    "    self_attn_mask = inputs_mask[i].reshape(1, -1).repeat_interleave(inputs_ids.shape[-1], dim=0)\n",
    "    print(self_attn_mask)\n",
    "    print(\"-\" * 50)\n",
    "\n",
    "    look_ahead_mask = generate_square_subsequent_mask(inputs_ids.shape[-1])\n",
    "\n",
    "    fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axs[0].matshow(self_attn_mask)\n",
    "    axs[0].set_title(\"self_attn_mask\")\n",
    "    axs[0].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_ylabel(\"querys\")\n",
    "    axs[0].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[0].set_xlabel(\"keys\")\n",
    "    axs[1].matshow(look_ahead_mask)\n",
    "    axs[1].set_title(\"look_ahead_mask\")\n",
    "    axs[1].set_yticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_ylabel(\"querys\")\n",
    "    axs[1].set_xticks(range(len(decode_text)), decode_text, fontsize=6)\n",
    "    axs[1].set_xlabel(\"keys\")\n",
    "    plt.show()\n",
    "    print('-' * 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c5b12ee51329e8",
   "metadata": {},
   "source": [
    "## Transformer Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "89a32ab3ca75bf0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.215535Z",
     "start_time": "2025-02-06T14:04:31.200067Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:32.811706Z",
     "iopub.status.busy": "2025-02-07T10:26:32.811405Z",
     "iopub.status.idle": "2025-02-07T10:26:32.839130Z",
     "shell.execute_reply": "2025-02-07T10:26:32.838210Z",
     "shell.execute_reply.started": "2025-02-07T10:26:32.811679Z"
    }
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class TransformerOutput:\n",
    "    logits: Tensor\n",
    "    encoder_last_hidden_states: Tensor\n",
    "    encoder_attn_scores: List[Tensor]  #画图\n",
    "    decoder_last_hidden_states: Tensor\n",
    "    decoder_self_attn_scores: List[Tensor]  #画图\n",
    "    decoder_cross_attn_scores: List[Tensor]  #画图\n",
    "    preds: Optional[Tensor] = None\n",
    "\n",
    "\n",
    "class TransformerModel(nn.Module):\n",
    "    def __init__(self, config):\n",
    "        super().__init__()\n",
    "        # hyper params\n",
    "        self.hidden_size = config[\"d_model\"]\n",
    "        self.num_encoder_layers = config[\"num_encoder_layers\"]\n",
    "        self.num_decoder_layers = config[\"num_decoder_layers\"]\n",
    "        self.pad_idx = config[\"pad_idx\"]\n",
    "        self.bos_idx = config[\"bos_idx\"]\n",
    "        self.eos_idx = config[\"eos_idx\"]\n",
    "        self.vocab_size = config[\"vocab_size\"]\n",
    "        self.dropout_rate = config[\"dropout\"]\n",
    "        self.max_length = config[\"max_length\"]\n",
    "        # 是否共享词嵌入，\n",
    "        # 共享后，decoder的词嵌入层和encoder的词嵌入层相同，节省内存\n",
    "        self.share = config[\"share_embedding\"]\n",
    "\n",
    "        # layers\n",
    "        self.src_embedding = TransformerEmbedding(config)  # 输入的嵌入层\n",
    "        # 如果共享词嵌入，则使用src_embedding作为trg_embedding\n",
    "        if self.share:\n",
    "            # 源和目标的嵌入层相同，共享参数，节省内存\n",
    "            self.trg_embedding = self.src_embedding\n",
    "            self.linear = lambda x: torch.matmul(\n",
    "                # x是该层的输入，[batch_size, seq_len, hidden_size]\n",
    "                # self.trg_embedding.get_word_embedding_weight().T: [hidden_size,vocab_size]\n",
    "                x, self.trg_embedding.get_word_embedding_weight().T\n",
    "            )  # 输出层，共享参数，直接拿原有embedding矩阵的转置，节省内存\n",
    "        else:\n",
    "            self.trg_embedding = TransformerEmbedding(config)  # decoder模块的嵌入层\n",
    "            self.linear = nn.Linear(self.hidden_size, self.vocab_size)  # 输出层\n",
    "\n",
    "        self.encoder = TransformerEncoder(config)\n",
    "        self.decoder = TransformerDecoder(config)\n",
    "\n",
    "        self._init_weights()  # init weights\n",
    "\n",
    "    def _init_weights(self):\n",
    "        # self.parameters(): 这个方法返回模型中的所有可学习参数（权重和偏置）。\n",
    "        for p in self.parameters():\n",
    "            if p.dim() > 1:  # 如果参数是二维或更高维（通常是权重矩阵），则执行初始化。\n",
    "                nn.init.xavier_uniform_(p)  # 使用 xavier 均匀分布来初始化权重\n",
    "\n",
    "    def generate_square_subsequent_mask(self, sz: int) -> Tensor:\n",
    "        # 为了生成斜三角的mask\n",
    "        mask = (torch.triu(torch.ones(sz, sz)) == 0).transpose(-1, -2).bool()\n",
    "        return mask\n",
    "\n",
    "    # 用于训练,在TransformerBlock中的每一层内是并行的，层间是串行的\n",
    "    def forward(\n",
    "            self, encoder_inputs, decoder_inputs, encoder_inputs_mask=None\n",
    "    ) -> TransformerOutput:\n",
    "        # encoder_inputs: [batch_size, src_len]\n",
    "        # decoder_inputs: [batch_size, trg_len]\n",
    "        # encoder_inputs_mask: [batch_size, src_len]\n",
    "        if encoder_inputs_mask is None:\n",
    "            # 返回一个布尔张量，形状与 encoder_inputs 相同。\n",
    "            # 该布尔张量的每个位置会根据 encoder_inputs 中的值是否等于 self.pad_idx 来设置：\n",
    "            # 如果相等，值为 True（表示该位置是填充位置）。\n",
    "            # 如果不相等，值为 False（表示该位置是有效输入） \n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        # unsqueeze(1): 这个操作在张量的第 1 个维度上增加一个新的维度\n",
    "        #    得到 (batch_size, 1, src_len)。\n",
    "        # unsqueeze(2): 这个操作在张量的第 2 个维度上再次增加一个新的维度。\n",
    "        #    得到 (batch_size, 1, 1, src_len)。\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(decoder_inputs.shape[-1])  # [trg_len, trg_len]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(decoder_inputs.device)\n",
    "        )  # [1, 1, trg_len, trg_len] 用于decoder的自注意力\n",
    "\n",
    "        # 增加decoder_inputs_mask和look_ahead_mask进行组合\n",
    "        decoder_inputs_mask = decoder_inputs.eq(self.pad_idx)\n",
    "        # [batch_size, trg_len]，和上面encoder_inputs_mask一致\n",
    "        decoder_inputs_mask = decoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, trg_len]\n",
    "        decoder_inputs_mask = decoder_inputs_mask + look_ahead_mask\n",
    "        # [batch_size, 1, 1, trg_len]与[1, 1, trg_len, trg_len]相加，得到decoder的自注意力mask\n",
    "        # decoder_inputs_mask : [batch_size, 1, trg_len,trg_len]\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        encoder_outputs = self.encoder(\n",
    "            encoder_inputs_embeds, attn_mask=encoder_inputs_mask\n",
    "        )  # encoder_inputs_mask用于encoder的自注意力,广播去做计算 \n",
    "\n",
    "        # decoding\n",
    "        # decoder_inputs_embeds是TransformerEmbedding(config)\n",
    "        decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "        # decoder是TransformerDecoder(config)\n",
    "        decoder_outputs = self.decoder(\n",
    "            decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "            encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "            attn_mask=decoder_inputs_mask,  # 用于decoder的自注意力,广播去做计算\n",
    "            cross_attn_mask=encoder_inputs_mask,  # 用于decoder的交叉注意力,广播去做计算\n",
    "        )\n",
    "        # decoder_outputs.last_hidden_states: [batch_size, trg_len, hidden_size]\n",
    "        logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "        # [batch_size, trg_len, vocab_size]\n",
    "\n",
    "        return TransformerOutput(\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n",
    "\n",
    "    @torch.no_grad()  # 禁用梯度计算\n",
    "    def infer(self, encoder_inputs, encoder_inputs_mask=None) -> TransformerOutput:\n",
    "        # 应对多个样本同时进行推理\n",
    "        if encoder_inputs_mask is None:\n",
    "            encoder_inputs_mask = encoder_inputs.eq(self.pad_idx)\n",
    "            # encoder_inputs_mask: [batch_size, src_len]\n",
    "        encoder_inputs_mask = encoder_inputs_mask.unsqueeze(1).unsqueeze(2)\n",
    "        # [batch_size, 1, 1, src_len],用于encoder的自注意力\n",
    "\n",
    "        look_ahead_mask = self.generate_square_subsequent_mask(self.max_length)\n",
    "        # [max_length, max_length]\n",
    "        look_ahead_mask = (\n",
    "            look_ahead_mask.unsqueeze(0).unsqueeze(0).to(encoder_inputs.device)\n",
    "        )  # [1, 1, max_length, max_length] 用于decoder的自注意力\n",
    "\n",
    "        # encoding\n",
    "        # src_embedding是TransformerEmbedding(config)\n",
    "        encoder_inputs_embeds = self.src_embedding(encoder_inputs)\n",
    "        # encoder是TransformerEncoder(config)\n",
    "        # 因为只支持单样本预测，没有paddings，所以不需要mask\n",
    "        encoder_outputs = self.encoder(encoder_inputs_embeds)\n",
    "        # encoder_outputs.last_hidden_states: [batch_size, src_len, hidden_size]\n",
    "\n",
    "        # decoding,多样本推理\n",
    "        # encoder_inputs.shape[0]: 获取编码器输入的批量大小（batch size），即样本的数量。\n",
    "        # .reshape(-1, 1): 这个操作将张量的形状调整为 (batch_size, 1)，\n",
    "        decoder_inputs = torch.Tensor([self.bos_idx] * encoder_inputs.shape[0]).reshape(-1, 1).long().to(\n",
    "            device=encoder_inputs.device)\n",
    "        # 推理是串行的，每次只推理一个token，所以需要初始化decoder_inputs为bos_idx\n",
    "        for cur_len in tqdm(range(1, self.max_length + 1)):\n",
    "            decoder_inputs_embeds = self.trg_embedding(decoder_inputs)\n",
    "            decoder_outputs = self.decoder(\n",
    "                decoder_inputs_embeds=decoder_inputs_embeds,\n",
    "                encoder_outputs=encoder_outputs.last_hidden_states,\n",
    "                # decoder的自注意力mask\n",
    "                # 因为是串行的，所以每次只看前面cur_len个token\n",
    "                attn_mask=look_ahead_mask[:, :, :cur_len, :cur_len],\n",
    "            )\n",
    "            # decoder_outputs.last_hidden_states: [batch_size, cur_len, hidden_size]\n",
    "            logits = self.linear(decoder_outputs.last_hidden_states)\n",
    "            # logits: [batch_size, cur_len, vocab_size]\n",
    "            # 通过最大下标确定类别，[:, -1:]表示取最后一个结果\n",
    "            next_token = logits.argmax(dim=-1)[:, -1:]  # [batch_size, 1]\n",
    "            # 预测输出拼接到输入中\n",
    "            decoder_inputs = torch.cat([decoder_inputs, next_token], dim=-1)\n",
    "            # dcoder_inputs: [batch_size, cur_len+1]\n",
    "\n",
    "            # (decoder_inputs == self.eos_idx).sum(dim=-1)是判断样本中是否含有EOS标记\n",
    "            # all是每一个都为True，才会结束\n",
    "            if all((decoder_inputs == self.eos_idx).sum(dim=-1) > 0):\n",
    "                break\n",
    "\n",
    "        return TransformerOutput(\n",
    "            preds=decoder_inputs[:, 1:],  # 去掉bos_idx\n",
    "            logits=logits,\n",
    "            encoder_last_hidden_states=encoder_outputs.last_hidden_states,\n",
    "            encoder_attn_scores=encoder_outputs.attn_scores,\n",
    "            decoder_last_hidden_states=decoder_outputs.last_hidden_states,\n",
    "            decoder_self_attn_scores=decoder_outputs.self_attn_scores,\n",
    "            decoder_cross_attn_scores=decoder_outputs.cross_attn_scores,\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a740def7afa83f9",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18175b791877293",
   "metadata": {},
   "source": [
    "## 损失函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a2b1b4622966eb25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.222855Z",
     "start_time": "2025-02-06T14:04:31.217548Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:32.840559Z",
     "iopub.status.busy": "2025-02-07T10:26:32.840256Z",
     "iopub.status.idle": "2025-02-07T10:26:32.847022Z",
     "shell.execute_reply": "2025-02-07T10:26:32.846335Z",
     "shell.execute_reply.started": "2025-02-07T10:26:32.840530Z"
    }
   },
   "outputs": [],
   "source": [
    "class CrossEntropyWithPadding:\n",
    "    def __init__(self, config):\n",
    "        self.label_smoothing = config[\"label_smoothing\"]\n",
    "\n",
    "    def __call__(self, logits, labels, padding_mask=None):\n",
    "        # logits: [batch_size, seq_len, vocab_size]\n",
    "        # labels: [batch_size, seq_len]\n",
    "        # padding_mask: [batch_size, seq_len]\n",
    "        bs, seq_len, nc = logits.shape\n",
    "        loss = F.cross_entropy(\n",
    "            logits.reshape(bs * seq_len, nc),\n",
    "            labels.reshape(-1),\n",
    "            reduction='none',  # 不对损失进行归约（reduction\n",
    "            label_smoothing=self.label_smoothing\n",
    "        )  # label_smoothing表示随机将一个类别的概率设置为0.1，使得模型更加关注其他类别\n",
    "        # loss: [batch_size * seq_len, 1]\n",
    "\n",
    "        if padding_mask is None:\n",
    "            loss = loss.mean()  # 计算平均损失\n",
    "        else:\n",
    "            # 将padding_mask reshape成一维张量，mask部分为0，非mask部分为1\n",
    "            padding_mask = 1 - padding_mask.reshape(-1)\n",
    "            loss = torch.mul(loss, padding_mask).sum() / padding_mask.sum()\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab581173629b9cc",
   "metadata": {},
   "source": [
    "## 学习率衰减"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "39efc556d490f34e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.228130Z",
     "start_time": "2025-02-06T14:04:31.223857Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:32.848379Z",
     "iopub.status.busy": "2025-02-07T10:26:32.847899Z",
     "iopub.status.idle": "2025-02-07T10:26:32.853452Z",
     "shell.execute_reply": "2025-02-07T10:26:32.852803Z",
     "shell.execute_reply.started": "2025-02-07T10:26:32.848351Z"
    }
   },
   "outputs": [],
   "source": [
    "# NoamDecayScheduler 是一个自定义或外部定义的学习率衰减调度器类。\n",
    "# 它需要接收配置 config 作为参数，可能实现了特定的学习率衰减方案\n",
    "class NoamDecayScheduler:\n",
    "    def __init__(self, config):\n",
    "        self.d_model = config[\"d_model\"]\n",
    "        self.warmup_steps = config[\"warmup_steps\"]\n",
    "\n",
    "    # __call__ 方法是一个特殊的方法，用于使对象能够像函数一样被调用\n",
    "    def __call__(self, step):\n",
    "        step += 1\n",
    "        arg1 = step ** (-0.5)  # 4000步之后是arg1\n",
    "        arg2 = step * (self.warmup_steps ** (-1.5))  # 4000步之前是arg2\n",
    "        arg3 = self.d_model ** (-0.5)\n",
    "\n",
    "        return arg3 * np.minimum(arg1, arg2)  # minimum是取两个数中的较小值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3188d631aaa09057",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.334627Z",
     "start_time": "2025-02-06T14:04:31.229133Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:32.854351Z",
     "iopub.status.busy": "2025-02-07T10:26:32.854026Z",
     "iopub.status.idle": "2025-02-07T10:26:32.987462Z",
     "shell.execute_reply": "2025-02-07T10:26:32.986918Z",
     "shell.execute_reply.started": "2025-02-07T10:26:32.854331Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_learning_rate_schedule = NoamDecayScheduler({\"d_model\": 512, \"warmup_steps\": 4000})\n",
    "#下面是学习率的设计图\n",
    "plt.plot(temp_learning_rate_schedule(np.arange(0, 40000)))\n",
    "plt.ylabel(\"Leraning rate\")\n",
    "plt.xlabel(\"Train step\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90185dd5b91d52ad",
   "metadata": {},
   "source": [
    "## 优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a5061e4cd6464c1b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.339984Z",
     "start_time": "2025-02-06T14:04:31.335629Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:32.988612Z",
     "iopub.status.busy": "2025-02-07T10:26:32.988343Z",
     "iopub.status.idle": "2025-02-07T10:26:32.992851Z",
     "shell.execute_reply": "2025-02-07T10:26:32.992308Z",
     "shell.execute_reply.started": "2025-02-07T10:26:32.988594Z"
    }
   },
   "outputs": [],
   "source": [
    "from torch.optim.lr_scheduler import LambdaLR\n",
    "from torch.optim import Adam\n",
    "\n",
    "\n",
    "def get_optimizer(model, config):\n",
    "    base_lr = 0.1\n",
    "    beta1 = config[\"beta1\"]  # Adam 的 beta1\n",
    "    beta2 = config[\"beta2\"]  # Adam 的 beta2\n",
    "    eps = config[\"eps\"]\n",
    "    optimizer = Adam(model.parameters(), lr=base_lr, betas=(beta1, beta2), eps=eps)\n",
    "    # config是一个字典，包含了学习率衰减的参数\n",
    "    lr_scheduler = NoamDecayScheduler(config)\n",
    "    # 使用 LambdaLR 调度器，它可以根据给定的函数 lr_lambda 调整学习率。\n",
    "    # 这里将 lr_scheduler 作为函数传递给 LambdaLR，它包含了特定于模型或任务的学习率调度规则\n",
    "    scheduler = LambdaLR(optimizer, lr_lambda=lr_scheduler)\n",
    "    return optimizer, scheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49a2f5433a409089",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.346703Z",
     "start_time": "2025-02-06T14:04:31.340986Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:32.993835Z",
     "iopub.status.busy": "2025-02-07T10:26:32.993595Z",
     "iopub.status.idle": "2025-02-07T10:26:32.998921Z",
     "shell.execute_reply": "2025-02-07T10:26:32.998431Z",
     "shell.execute_reply.started": "2025-02-07T10:26:32.993817Z"
    }
   },
   "outputs": [],
   "source": [
    "class SaveCheckpointsCallback:\n",
    "    def __init__(self, save_dir, save_step=5000, save_best_only=True):\n",
    "        self.save_dir = save_dir  # 保存路径\n",
    "        self.save_step = save_step  # 保存步数\n",
    "        self.save_best_only = save_best_only  # 是否只保存最好的模型\n",
    "        self.best_metric = -np.inf  # 最好的指标，指标不可能为负数，所以初始化为-1\n",
    "        # 创建保存路径\n",
    "        if not os.path.exists(self.save_dir):  # 如果不存在保存路径，则创建\n",
    "            os.makedirs(self.save_dir)\n",
    "\n",
    "    # 对象被调用时：当你将对象像函数一样调用时，Python 会自动调用 __call__ 方法。\n",
    "    # state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "    # metric 是指标，可以是验证集的准确率，也可以是其他指标\n",
    "    def __call__(self, step, state_dict, metric=None):\n",
    "        if step % self.save_step > 0:\n",
    "            return  # 不是保存步数，则直接返回\n",
    "\n",
    "        if self.save_best_only:\n",
    "            assert metric is not None  # 必须传入metric\n",
    "            if metric >= self.best_metric:  # 如果当前指标大于最好的指标\n",
    "                # save checkpoint\n",
    "                # 保存最好的模型，覆盖之前的模型，不保存step，只保存state_dict，即模型参数，不保存优化器参数\n",
    "                torch.save(state_dict, os.path.join(self.save_dir, \"nums_head_16.ckpt\"))\n",
    "                self.best_metric = metric  # 更新最好的指标\n",
    "        else:\n",
    "            # 保存模型\n",
    "            torch.save(state_dict, os.path.join(self.save_dir, f\"{step}.ckpt\"))\n",
    "            # 保存每个step的模型，不覆盖之前的模型，保存step，保存state_dict，即模型参数，不保存优化器参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1c98380e43240291",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.352748Z",
     "start_time": "2025-02-06T14:04:31.347708Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:32.999993Z",
     "iopub.status.busy": "2025-02-07T10:26:32.999525Z",
     "iopub.status.idle": "2025-02-07T10:26:33.003900Z",
     "shell.execute_reply": "2025-02-07T10:26:33.003400Z",
     "shell.execute_reply.started": "2025-02-07T10:26:32.999974Z"
    }
   },
   "outputs": [],
   "source": [
    "class EarlyStopCallback:\n",
    "    def __init__(self, patience=5, min_delta=0.01):\n",
    "        self.patience = patience  # 多少个step没有提升就停止训练\n",
    "        self.min_delta = min_delta  # 最小的提升幅度\n",
    "        self.best_metric = -np.inf  # 记录的最好的指标\n",
    "        self.counter = 0  # 计数器，记录连续多少个step没有提升\n",
    "\n",
    "    def __call__(self, metric):\n",
    "        if metric >= self.best_metric + self.min_delta:  # 如果指标提升了\n",
    "            self.best_metric = metric  # 更新最好的指标\n",
    "            self.counter = 0  # 计数器清零\n",
    "        else:\n",
    "            self.counter += 1  # 计数器加一\n",
    "\n",
    "    @property  # 使用@property装饰器，使得 对象.early_stop可以调用，不需要()\n",
    "    def early_stop(self):\n",
    "        # 如果计数器大于等于patience，则返回True，停止训练\n",
    "        return self.counter >= self.patience"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebb3c8c230d312c1",
   "metadata": {},
   "source": [
    "## training & valuating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "140f2acfbeb9740a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.358185Z",
     "start_time": "2025-02-06T14:04:31.353752Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:33.004753Z",
     "iopub.status.busy": "2025-02-07T10:26:33.004531Z",
     "iopub.status.idle": "2025-02-07T10:26:33.008876Z",
     "shell.execute_reply": "2025-02-07T10:26:33.008328Z",
     "shell.execute_reply.started": "2025-02-07T10:26:33.004735Z"
    }
   },
   "outputs": [],
   "source": [
    "@torch.no_grad()  # 禁用梯度计算\n",
    "def evaluating(model, data_loader, loss_fct):\n",
    "    loss_list = []\n",
    "    for batch in data_loader:\n",
    "        encoder_inputs = batch[\"encoder_inputs\"]\n",
    "        encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "        decoder_inputs = batch[\"decoder_inputs\"]\n",
    "        decoder_labels = batch[\"decoder_labels\"]\n",
    "        decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "        # 前向计算\n",
    "        outputs = model(\n",
    "            encoder_inputs=encoder_inputs,\n",
    "            decoder_inputs=decoder_inputs,\n",
    "            encoder_inputs_mask=encoder_inputs_mask,\n",
    "        )\n",
    "        logits = outputs.logits\n",
    "        # 计算损失\n",
    "        loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "        loss_list.append(loss.cpu().item())\n",
    "\n",
    "    return np.mean(loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3099e48677dcb6bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.366317Z",
     "start_time": "2025-02-06T14:04:31.359188Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:33.009890Z",
     "iopub.status.busy": "2025-02-07T10:26:33.009591Z",
     "iopub.status.idle": "2025-02-07T10:26:33.017326Z",
     "shell.execute_reply": "2025-02-07T10:26:33.016779Z",
     "shell.execute_reply.started": "2025-02-07T10:26:33.009863Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def training(model,\n",
    "             train_loader,\n",
    "             val_loader,\n",
    "             epoch,\n",
    "             loss_fct,\n",
    "             optimizer,\n",
    "             scheduler=None,\n",
    "             save_ckpt_callback=None,\n",
    "             early_stop_callback=None,\n",
    "             eval_step=500,\n",
    "             ):\n",
    "    record_dict = {\n",
    "        \"train\": [],\n",
    "        \"val\": []\n",
    "    }\n",
    "\n",
    "    global_step = 1  # 全局步数\n",
    "    model.train()  # 训练模式\n",
    "    with tqdm(total=epoch * len(train_loader)) as pbar:\n",
    "        for epoch_id in range(epoch):\n",
    "            # 训练\n",
    "            for batch in train_loader:\n",
    "                encoder_inputs = batch[\"encoder_inputs\"]\n",
    "                encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "                decoder_inputs = batch[\"decoder_inputs\"]\n",
    "                decoder_labels = batch[\"decoder_labels\"]\n",
    "                decoder_labels_mask = batch[\"decoder_labels_mask\"]\n",
    "\n",
    "                optimizer.zero_grad()  # 梯度清零\n",
    "\n",
    "                # 前向传播\n",
    "                outputs = model(\n",
    "                    encoder_inputs=encoder_inputs,\n",
    "                    decoder_inputs=decoder_inputs,\n",
    "                    encoder_inputs_mask=encoder_inputs_mask\n",
    "                )\n",
    "                logits = outputs.logits\n",
    "                loss = loss_fct(logits, decoder_labels, padding_mask=decoder_labels_mask)\n",
    "\n",
    "                # 梯度回传\n",
    "                loss.backward()\n",
    "\n",
    "                # 调整优化器，包括学习率的变动等\n",
    "                optimizer.step()\n",
    "                if scheduler is not None:\n",
    "                    scheduler.step()  # 更新学习率\n",
    "\n",
    "                loss = loss.cpu().item()\n",
    "\n",
    "                record_dict[\"train\"].append({\n",
    "                    \"loss\": loss,\n",
    "                    \"step\": global_step\n",
    "                })\n",
    "\n",
    "                # 评估\n",
    "                if global_step % eval_step == 0:\n",
    "                    model.eval()  # 评估模式\n",
    "                    # 验证集损失和准确率\n",
    "                    val_loss = evaluating(model, val_loader, loss_fct)\n",
    "                    record_dict[\"val\"].append({\n",
    "                        \"loss\": val_loss,\n",
    "                        \"step\": global_step\n",
    "                    })\n",
    "                    model.train()  # 训练模式\n",
    "\n",
    "                    # 2. 保存模型权重 save model checkpoint\n",
    "                    if save_ckpt_callback is not None:\n",
    "                        # model.state_dict() 返回模型参数的字典，包括模型参数和优化器参数\n",
    "                        save_ckpt_callback(global_step, model.state_dict(), -val_loss)\n",
    "                        # 保存最好的模型，覆盖之前的模型，保存step，保存state_dict,通过metric判断是否保存最好的模型\n",
    "\n",
    "                    # 3. 早停 early stopping\n",
    "                    if early_stop_callback is not None:\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        early_stop_callback(-val_loss)\n",
    "                        # 验证集准确率不再提升，则停止训练\n",
    "                        if early_stop_callback.early_stop:\n",
    "                            print(f\"Early stop at epoch {epoch_id} / global_step {global_step}\")\n",
    "                            return record_dict  # 早停，返回记录字典 record_dict\n",
    "\n",
    "                # 更新进度条和全局步数\n",
    "                pbar.update(1)  # 更新进度条\n",
    "                global_step += 1  # 全局步数加一\n",
    "            pbar.set_postfix({\"epoch\": epoch_id, \"loss\": loss, \"val_loss\": val_loss})\n",
    "\n",
    "    return record_dict  # 训练结束，返回记录字典 record_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cf04ac5e18afea4f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:31.441762Z",
     "start_time": "2025-02-06T14:04:31.367320Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:33.018342Z",
     "iopub.status.busy": "2025-02-07T10:26:33.017913Z",
     "iopub.status.idle": "2025-02-07T10:26:33.322093Z",
     "shell.execute_reply": "2025-02-07T10:26:33.321351Z",
     "shell.execute_reply.started": "2025-02-07T10:26:33.018322Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load train dataset from wmt16/.cache/de2en_train_128.npy\n",
      "load val dataset from wmt16/.cache/de2en_val_128.npy\n"
     ]
    }
   ],
   "source": [
    "#模型的超参\n",
    "config = {\n",
    "    \"bos_idx\": 1,\n",
    "    \"eos_idx\": 3,\n",
    "    \"pad_idx\": 0,\n",
    "    \"vocab_size\": len(word2idx),\n",
    "    \"max_length\": 128,\n",
    "    \"d_model\": 512, # 可调整\n",
    "    \"dim_feedforward\": 2048,  # FFN 的隐藏层大小\n",
    "    \"dropout\": 0.1, # 可调整\n",
    "    \"layer_norm_eps\": 1e-6,  # 层归一化的 epsilon, 防止除零错误\n",
    "    \"num_heads\": 16, # 可调整\n",
    "    \"num_decoder_layers\": 6, # 可调整\n",
    "    \"num_encoder_layers\": 6, # 可调整\n",
    "    \"label_smoothing\": 0.1,\n",
    "    \"beta1\": 0.9,  # Adam 的 beta1\n",
    "    \"beta2\": 0.98,  # Adam 的 beta2\n",
    "    \"eps\": 1e-9,\n",
    "    \"warmup_steps\": 4_000,\n",
    "    \"share_embedding\": False,  # 是否共享词向量\n",
    "}\n",
    "\n",
    "\n",
    "def get_dl(dataset, batch_size, shuffle=True):\n",
    "    sampler = TransformerBatchSampler(dataset, batch_size=batch_size, shuffle_batch=shuffle)\n",
    "    sample_dl = DataLoader(dataset, batch_sampler=sampler, collate_fn=partial(collate_fn, tokenizer=tokenizer))\n",
    "    return sample_dl\n",
    "\n",
    "\n",
    "# dataset\n",
    "train_ds = LangPairDataset(\"train\", max_length=config[\"max_length\"])\n",
    "val_ds = LangPairDataset(\"val\", max_length=config[\"max_length\"])\n",
    "\n",
    "# tokenizer\n",
    "tokenizer = Tokenizer(word2idx=word2idx, idx2word=idx2word, max_length=config[\"max_length\"])\n",
    "\n",
    "# dataloader\n",
    "batch_size = 2048\n",
    "train_dl = get_dl(train_ds, batch_size=batch_size, shuffle=True)\n",
    "val_dl = get_dl(val_ds, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c4bd1cb45f5032ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:32.221751Z",
     "start_time": "2025-02-06T14:04:31.442764Z"
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:33.323336Z",
     "iopub.status.busy": "2025-02-07T10:26:33.322930Z",
     "iopub.status.idle": "2025-02-07T10:26:34.217838Z",
     "shell.execute_reply": "2025-02-07T10:26:34.217091Z",
     "shell.execute_reply.started": "2025-02-07T10:26:33.323299Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型参数量: 71938239\n"
     ]
    }
   ],
   "source": [
    "#计算模型参数量\n",
    "model = TransformerModel(config)\n",
    "print(f\"模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6b87e4917b9ef89b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-06T14:04:53.810386Z",
     "start_time": "2025-02-06T14:04:32.224757Z"
    },
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-02-07T10:26:34.219230Z",
     "iopub.status.busy": "2025-02-07T10:26:34.218820Z",
     "iopub.status.idle": "2025-02-07T10:53:00.968758Z",
     "shell.execute_reply": "2025-02-07T10:53:00.967922Z",
     "shell.execute_reply.started": "2025-02-07T10:26:34.219193Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "33499it [26:24, 21.14it/s, epoch=31, loss=2.49, val_loss=3.46]                        "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early stop at epoch 32 / global_step 33500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "epoch = 100\n",
    "\n",
    "# model\n",
    "model = TransformerModel(config)\n",
    "model = model.to(device)\n",
    "\n",
    "# 1. 定义损失函数 采用交叉熵损失\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "# 2. 定义优化器\n",
    "optimizer, scheduler = get_optimizer(model, config)\n",
    "\n",
    "# 3.save model checkpoint\n",
    "if not os.path.exists(\"checkpoints\"):\n",
    "    os.makedirs(\"checkpoints\")\n",
    "save_ckpt_callback = SaveCheckpointsCallback(save_dir=\"checkpoints\", save_step=500, save_best_only=True)\n",
    "\n",
    "# 4. early stopping\n",
    "early_stop_callback = EarlyStopCallback(patience=10,min_delta=0.001)\n",
    "\n",
    "record_dict = training(\n",
    "    model,\n",
    "    train_dl,\n",
    "    val_dl,\n",
    "    epoch,\n",
    "    loss_fct,\n",
    "    optimizer,\n",
    "    scheduler,\n",
    "    save_ckpt_callback=save_ckpt_callback,\n",
    "    early_stop_callback=early_stop_callback,\n",
    "    eval_step=500\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ac7e66d5df05c3",
   "metadata": {},
   "source": [
    "# 推理\n",
    "\n",
    "- 翻译项目的评估指标一般是BLEU4\n",
    "- 接下来进行翻译推理，并作出注意力的热度图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "305c0410623a9f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T10:53:00.970701Z",
     "iopub.status.busy": "2025-02-07T10:53:00.970206Z",
     "iopub.status.idle": "2025-02-07T10:53:01.178672Z",
     "shell.execute_reply": "2025-02-07T10:53:01.178013Z",
     "shell.execute_reply.started": "2025-02-07T10:53:00.970676Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "# 加载模型\n",
    "state_dict = torch.load(f\"checkpoints/nums_head_16.ckpt\", map_location=\"cpu\",weights_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6f512e7c8f2db972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T10:53:01.179767Z",
     "iopub.status.busy": "2025-02-07T10:53:01.179460Z",
     "iopub.status.idle": "2025-02-07T10:53:16.653647Z",
     "shell.execute_reply": "2025-02-07T10:53:16.653010Z",
     "shell.execute_reply.started": "2025-02-07T10:53:01.179745Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load test dataset from wmt16/.cache/de2en_test_128.npy\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 4-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "7it [00:00, 69.67it/s]/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 2-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "/usr/local/lib/python3.10/site-packages/nltk/translate/bleu_score.py:577: UserWarning: \n",
      "The hypothesis contains 0 counts of 3-gram overlaps.\n",
      "Therefore the BLEU score evaluates to 0, independently of\n",
      "how many N-gram overlaps of lower order it contains.\n",
      "Consider using lower n-gram order or use SmoothingFunction()\n",
      "  warnings.warn(_msg)\n",
      "966it [00:14, 67.49it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testing loss: 3.332251434494003\n",
      "testing bleu: 0.58512884807825\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "\n",
    "loss_fct = CrossEntropyWithPadding(config)\n",
    "\n",
    "test_ds = LangPairDataset(\"test\", max_length=config[\"max_length\"])\n",
    "test_dl = DataLoader(\n",
    "    test_ds, batch_size=1,\n",
    "    collate_fn=partial(collate_fn, tokenizer=tokenizer)\n",
    ")\n",
    "\n",
    "model = model.to(device)\n",
    "model.eval()\n",
    "collect = {}\n",
    "loss_collect = []\n",
    "\n",
    "predictions = []\n",
    "answers = []\n",
    "\n",
    "# 初始化BLEU分数列表\n",
    "bleu_scores = []\n",
    "for idx, batch in tqdm(enumerate(test_dl)):\n",
    "    encoder_inputs = batch[\"encoder_inputs\"]\n",
    "    encoder_inputs_mask = batch[\"encoder_inputs_mask\"]\n",
    "    decoder_inputs = batch[\"decoder_inputs\"]\n",
    "    decoder_labels = batch[\"decoder_labels\"]\n",
    "\n",
    "    # 前向计算\n",
    "    outputs = model(\n",
    "        encoder_inputs=encoder_inputs,\n",
    "        decoder_inputs=decoder_inputs,\n",
    "        encoder_inputs_mask=encoder_inputs_mask\n",
    "    )\n",
    "\n",
    "    loss = loss_fct(outputs.logits, decoder_labels)  # 验证集损失\n",
    "\n",
    "    preds = outputs.logits.argmax(dim=-1)  # 预测结果，[1,seq_len]\n",
    "    # 把preds转为英文单词\n",
    "    preds = tokenizer.decode(preds.cpu().numpy())  #['预测句子']\n",
    "    #把decoder_labels转为英文单词\n",
    "    decoder_labels = tokenizer.decode(decoder_labels.cpu().numpy())  #['标签句子']\n",
    "\n",
    "    answers.append(decoder_labels)\n",
    "\n",
    "    belu = sentence_bleu([decoder_labels[0].split()], preds[0].split(), weights=(1, 0, 0, 0))\n",
    "    bleu_scores.append(belu)\n",
    "    collect[idx] = {\n",
    "        \"loss\": loss.item(),\n",
    "        \"src_inputs\": encoder_inputs,\n",
    "        \"trg_inputs\": decoder_inputs,\n",
    "        \"mask\": encoder_inputs_mask,\n",
    "        \"trg_labels\": decoder_labels,\n",
    "        \"preds\": preds\n",
    "    }\n",
    "    loss_collect.append(loss.item())\n",
    "\n",
    "# sort collect by value\n",
    "collect = sorted(collect.items(), key=lambda x: x[1][\"loss\"])\n",
    "print(f\"testing loss: {np.array(loss_collect).mean()}\")\n",
    "belu_scores = sum(bleu_scores) / len(bleu_scores)\n",
    "print(f\"testing bleu: {belu_scores}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a9ab2039d24b72a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T10:53:16.654941Z",
     "iopub.status.busy": "2025-02-07T10:53:16.654454Z",
     "iopub.status.idle": "2025-02-07T10:53:16.658141Z",
     "shell.execute_reply": "2025-02-07T10:53:16.657462Z",
     "shell.execute_reply.started": "2025-02-07T10:53:16.654917Z"
    }
   },
   "outputs": [],
   "source": [
    "# !pip install Cython  # if failed to install fastBPE, try this line\n",
    "# !pip install fastBPE -v\n",
    "# 在 Windows 系统上并没有 sys/mman.h 文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1e7c1e0999eb365d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T10:53:16.659785Z",
     "iopub.status.busy": "2025-02-07T10:53:16.659297Z",
     "iopub.status.idle": "2025-02-07T10:53:17.444045Z",
     "shell.execute_reply": "2025-02-07T10:53:17.443350Z",
     "shell.execute_reply.started": "2025-02-07T10:53:16.659749Z"
    }
   },
   "outputs": [],
   "source": [
    "import re\n",
    "from fastBPE import fastBPE\n",
    "from sacremoses import MosesDetokenizer, MosesTokenizer\n",
    "\n",
    "# `MosesTokenizer` 和 `MosesDetokenizer` 是来自 `sacremoses` 库的工具，用于自然语言处理中的分词（Tokenization）和去标记化（Detokenization）。\n",
    "# 这些工具主要用于对文本进行预处理和后处理，通常在处理自然语言处理任务时会用到。\n",
    "\n",
    "# 这些工具通常在文本预处理和后处理过程中使用，对输入的文本进行标记化和去标记化，是一种常用的处理方式。\n",
    "# 在自然语言处理任务中，对文本进行正确的分词和还原是很重要的，而 `MosesTokenizer` 和 `MosesDetokenizer` 提供了方便、高效的工具来处理这些任务。\n",
    "\n",
    "class Translator:\n",
    "    def __init__(self, model, src_tokenizer, trg_tokenizer):\n",
    "        self.bpe = fastBPE(\"./wmt16/bpe.20000\", \"./wmt16/vocab\")\n",
    "        self.mose_tokenizer = MosesTokenizer(lang=\"de\")\n",
    "        self.mose_detokenizer = MosesDetokenizer(lang=\"en\")\n",
    "        self.model = model\n",
    "        self.model.eval()\n",
    "        self.src_tokenizer = src_tokenizer\n",
    "        self.trg_tokenizer = trg_tokenizer\n",
    "        # 匹配出现的 @@ （后面带空格的情况）。\n",
    "        # 或者匹配以 @@ 结尾的情况（可选空格）。\n",
    "        self.pattern = re.compile(r'(@@ )|(@@ ?$)')\n",
    "        \n",
    "    def draw_attention_map(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "        attn_scores (numpy.ndarray): 表示自注意力机制（self-attention）分数。\n",
    "        cross_attn_scores (numpy.ndarray): 表示交叉注意力机制的注意力分数。\n",
    "        src_words_list (list): 源语言句子的单词列表。\n",
    "        trg_words_list (list): 目标语言句子的单词列表。\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "\n",
    "        fig = plt.figure(figsize=(10, 5), constrained_layout=True) # constrained_layout=True 自动调整子图参数，使之填充整个图像区域\n",
    "        grid = plt.GridSpec(trg_len, trg_len + src_len, wspace=0.1, hspace=0.1)# wspace,hspace 控制子图之间的间距\n",
    "        #下面是attn_scores的热力图\n",
    "        self_map = fig.add_subplot(grid[:,:trg_len]) #  添加子图\n",
    "        self_map.matshow(attn_scores.mean(dim=0), cmap='viridis') # 绘制热力图，cmap表示颜色,dim=0表示对第0维求均值\n",
    "        self_map.set_yticks(range(trg_len), trg_words_list, fontsize=10)\n",
    "        self_map.set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "        #下面是cross_attn_scores的热力图\n",
    "        cross_map = fig.add_subplot(grid[:, trg_len:])\n",
    "        cross_map.matshow(cross_attn_scores.mean(dim=0), cmap='viridis')\n",
    "        cross_map.set_yticks(range(trg_len), [], fontsize=6)\n",
    "        cross_map.set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "    def draw_attention_maps(self, attn_scores, cross_attn_scores, src_words_list, trg_words_list, heads_list):\n",
    "        \"\"\"绘制注意力热力图\n",
    "\n",
    "        Args:\n",
    "            - scores (numpy.ndarray): shape = [source sequence length, target sequence length]\n",
    "        \"\"\"\n",
    "        assert len(attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, target sequence length], but got {attn_scores.shape}\"\n",
    "        attn_scores = attn_scores[:, :len(trg_words_list), :len(trg_words_list)]\n",
    "\n",
    "        assert len(cross_attn_scores.shape) == 3, \"attn_scores shape should be \" \\\n",
    "            f\"[num heads, target sequence length, source sequence length], but got {cross_attn_scores.shape}\"\n",
    "        cross_attn_scores = cross_attn_scores[:, :len(trg_words_list), :len(src_words_list)]\n",
    "        # cross_attn_scores = cross_attn_scores[:, :len(src_words_list), :len(src_words_list)]\n",
    "\n",
    "        num_heads, trg_len, src_len = cross_attn_scores.shape\n",
    "        fig, axes = plt.subplots(2, len(heads_list), figsize=(5 * len(heads_list), 10))\n",
    "        for i, heads_idx in enumerate(heads_list):\n",
    "            axes[0, i].matshow(attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[0, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[0, i].set_xticks(range(trg_len), [\"[BOS]\"] + trg_words_list[:-1], rotation=90)\n",
    "            axes[0, i].set_title(f\"head {heads_idx}\")\n",
    "            axes[1, i].matshow(cross_attn_scores[heads_idx], cmap='viridis')\n",
    "            axes[1, i].set_yticks(range(trg_len), trg_words_list)\n",
    "            axes[1, i].set_xticks(range(src_len), src_words_list, rotation=90)\n",
    "            axes[1, i].set_title(f\"head {heads_idx}\")\n",
    "\n",
    "        plt.show()\n",
    "    \n",
    "    def __call__(self, sentence_list,heads_list=None,layer_idx=-1):\n",
    "        # 将输入句子列表转换为小写，并使用 MosesTokenizer 进行分词处理。\n",
    "        sentence_list = [\" \".join(self.mose_tokenizer.tokenize(s.lower())) for s in sentence_list]\n",
    "        # 将分词后的结果进行 BPE 编码，得到 tokens_list。\n",
    "        tokens_list = [s.split() for s in self.bpe.apply(sentence_list)]\n",
    "        \n",
    "        # 使用 src_tokenizer 对 tokens_list 进行编码，同时添加起始标记 ([BOS]) 和结束标记 ([EOS])。\n",
    "        encoder_input, attn_mask = self.src_tokenizer.encode(\n",
    "            tokens_list,\n",
    "            add_bos=True,\n",
    "            add_eos=True,\n",
    "            return_mask=True,\n",
    "            )\n",
    "        encoder_input = torch.Tensor(encoder_input).to(dtype=torch.int64)\n",
    "        \n",
    "        # 使用模型的 infer 方法对编码器输入进行推理，得到输出结果 outputs\n",
    "        outputs = model.infer(encoder_inputs=encoder_input, encoder_inputs_mask=attn_mask)\n",
    "        preds = outputs.preds.numpy() # 推理结果\n",
    "        \n",
    "        # 使用目标语言的 trg_tokenizer 对预测序列进行解码，得到解码后的目标语言句子列表 trg_decoded。\n",
    "        trg_decoded = self.trg_tokenizer.decode(preds, split=True, remove_eos=False, remove_bos=False, remove_pad=False)\n",
    "        # 使用源语言的 src_tokenizer 对编码器输入进行解码，得到解码后的源语言句子列表 src_decoded。为下面绘制热力图做准备。\n",
    "        src_decoded = self.src_tokenizer.decode(\n",
    "            encoder_input.numpy(),\n",
    "            split=True,\n",
    "            remove_bos=False,\n",
    "            remove_eos=False\n",
    "            )\n",
    "        \n",
    "         # draw the attention map of the last decoder block\n",
    "        for attn_score, cross_attn_score, src, trg in zip(\n",
    "            outputs.decoder_self_attn_scores[layer_idx], outputs.decoder_cross_attn_scores[layer_idx], src_decoded, trg_decoded):\n",
    "            if heads_list is None:# 如果没有指定heads_list，就画单个热力图\n",
    "                self.draw_attention_map(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                )\n",
    "            else:# 如果指定了heads_list，就画多个热力图\n",
    "                self.draw_attention_maps(\n",
    "                    attn_score,\n",
    "                    cross_attn_score,\n",
    "                    src,\n",
    "                    trg,\n",
    "                    heads_list=heads_list,\n",
    "                    )\n",
    "        \n",
    "        #将解码后的目标语言句子列表返回，并使用 mose_detokenizer 进行去标记化，最终得到翻译后的结果。\n",
    "        return [self.mose_detokenizer.tokenize(self.pattern.sub(\"\", s).split()) for s in self.trg_tokenizer.decode(preds)] \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c5d288cf83d67b62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-07T10:53:17.445164Z",
     "iopub.status.busy": "2025-02-07T10:53:17.444857Z",
     "iopub.status.idle": "2025-02-07T10:53:18.670974Z",
     "shell.execute_reply": "2025-02-07T10:53:18.670431Z",
     "shell.execute_reply.started": "2025-02-07T10:53:17.445144Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading vocabulary from ./wmt16/vocab ...\n",
      "Read 762259 words (18107 unique) from vocabulary file.\n",
      "Loading codes from ./wmt16/bpe.20000 ...\n",
      "Read 20001 codes from the codes file.\n",
      "  7%|▋         | 9/128 [00:00<00:01, 67.27it/s]\n",
      "/usr/local/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: There are no gridspecs with layoutgrids. Possibly did not call parent GridSpec with the \"figure\" keyword\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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2bdtq69ateu6557R582ZJUrdu3XTvvfc6Wlratm2r7Oxs9e7dW82bN3f8cCcUHoeR/Lly5cqd8edw3XXXOZxGevDBBzVlyhRNmzaNQ/n+X3x8vJKTk31uCSJJixYtUv369R3PM3nyZMfnmSc6OlpvvPGGa/M/m6VLl1pf5x2cxDLsf8svfIWFhWnOnDlnfP7U5bqocDjfBWDixInauXMnG28USkREhH744QfHD2dB4XEYyZ/Lzs7WpEmT9NZbb2nr1q2SpDp16qhnz5564IEH8u1lKGpdu3bV0qVLVbZsWTVo0CDf/OfPn+9oHn8wY8YMjRo1ShMnTlTfvn01Y8YM7dixQ4mJiZoxY4ZuvfVWtyPi/82ZM0dPPfWUdSWzOnXq6OGHH3b0Xmv+xt+W3+zsbCUlJWnHjh3q2bOnIiIi9L///U+RkZGO3FjWn33//fc+2wGnboTMnqhz4G8b7xUrVmjp0qX65JNP2Hj7ie+//77Q0zp91/MOHTpo2bJllCg/xmEkZ/f777/rqquu0ldffaVOnTrpyiuvlCRt2rRJjzzyiD788EMtXrzY5+IpRS0qKkpdu3Z1bH7FQb9+/RQWFqbHHntMmZmZ6tmzpypXrqwpU6Y49gY0PT1dkZGR1tdnkzddUZg6dar++c9/KjQ0NN/5sae7//77iyxHQZ555hmNGDFCAwYM8LkNx913361ff/1VgwcPLtL5DxkyRGPHjlXp0qX15ZdfqmXLlgoKcv/tqT8sv3n27Nmjv/3tb0pNTVVWVpauuuoqRURE6D//+Y+ysrIcPf/Tn+Rd3C0lJcVnL2qDBg00c+ZMXXrppUU6f/ZE2XT6xjtvl+6mTZv0+eefq1WrVo5vvO+8886zPv/qq686lAR58i7i8Gd/Xm7c9fzFF1/U6NGj1atXL11yySUqXbq0z/NOndSMM0tISFBiYqJuvPFGnz1Rzz77rF599VV99913bkd01eOPP65Zs2bpv//9b74PIdavX6+///3vuvPOOzVq1Ch3AiKfzMxMZWRkOH4F0FPvgXSmi+sYY4p8XRwfH6+1a9cqJibGuqBDQTwej+M3co2Pj9fo0aN1xx13+IyfPXu2Ro0aZZ07VVSCg4P1448/qmLFime8Z5Xb3Fp+8+QdlTBz5kzFxMRY24SkpCT179+/aO+F5KdSUlLUokUL1a9fX4MHD7bej6ekpGjSpEnasmWLvv766yI9xYYSZRMbbxTGnj17Cj3t6cdbF7WAgIAzPudGqUN+/nYYib+pW7euxo8fr5tuuqnA59955x09+uij1pECKLmWLVumVq1aKSgoSMuWLTvrtE5fFMpfhIaGasOGDfnOrd62bZsaNWqkY8eOFen8a9eurZtvvllXX3212rdvr/fff/+MF7nI2+tc0sTExGjVqlWqW7euzwdru3fvVkJCgjIzM92O6Libb75Z2dnZeu+99/J9OGKMUbdu3RQcHKy333676EIU6V2oLkB16tTxuanj6d5++21Tu3ZtBxMBuBC9/vrrplatWsbj8RiPx2OqVKliZsyY4XYsv+D1ek1qauoZn09NTTVer9fBRCe98847pnv37qZFixamWbNmPkNJtG/fPnPbbbeZ2NhYExgYaAICAnyGki4rK8ts3rzZnDhxwtUcDRo0MOPGjcs3fuzYsaZhw4ZFPv/333/fVKxY0Xg8HhMQEGCt804fnF5m/Gn5jYqKMhs3bjTGnLxp9I4dO4wxxixfvtxUqFDB0Sz+oly5cmbNmjVnfH716tWmXLlyRZrB/YNOi5k9e/bosssuO+Pzl19+uVJTUx1MdNK7776rt99+W6mpqTp+/LjPcyX90B83fPjhh4WelsPncLr09HT16tVLvXr1yncYyfbt20v81TgjIyO1f//+M15hct++fYqIiHA009SpU/Xoo4+qT58+WrBgge68807t2LFDa9as0X333edoFn/Rp08fpaamasSIEQXew9ANy5cv10svvaSdO3fqnXfeUZUqVfTaa68pPj7e58a3RSkzM1MDBw7U7NmzJZ28OXTNmjU1cOBAValSRUOHDnUkR57Ro0frlltu0ZdffmmdE7Vy5UotWbKkaD/F/39dunRRly5dlJGRocjISG3ZssUvDufzp+X36quv1uTJk/Xyyy9LOnnUSEZGhh5//PESe3ubI0eOqGLFimd8vlKlSjpy5EjRhijSinYBKl++vFm7du0Zn3ei+Z5uypQpJjw83AwYMMCEhISYf/3rX6ZTp06mTJkyZvjw4Y5mwUln+iTNrU/WpkyZYn7//Xfr67MNcF/r1q3NsWPH8o3fvHmzqVKliguJ/MvNN99sunXrdsbnu3XrZrp37+5gImPq1q1r3nzzTWOM7yfFI0aMMPfdd5+jWfxFeHi4WbdundsxLO+++64JCwsz/fr1M16v1/odPfvss+baa691LMf9999vLrnkErN8+XJTunRpK8cHH3xgmjZt6liOU61du9b06tXLXHzxxebiiy82vXr1Mt99953jOZKSklzfM5fHn5bftLQ0k5CQYOrXr2+CgoLM5ZdfbmJiYkzdunXNzz//7HY8V/zZkWHvvPOOqVOnTpFm4Jwom2655RbrGMyC3HTTTQoMDHTk05s89erV0+OPP64ePXr4HCs7cuRIHTx4UNOmTXMsC/yTP5/UjPyuvfZaeTweffjhh9ZVqjZt2qQOHTro5ptv1pQpU1xO6K68E4obNGigIUOGqF69ejLGaNOmTZo0aZJSUlL09ddfq0GDBo5lKlWqlDZt2qTq1aurQoUK+uyzz9SkSRNt27ZNl19+uQ4cOOBYFn+RkJCgN954o8ArTbqhWbNmGjx4sO644w6fbeW6det07bXXat++fY7kqF69uubNm6fLL7/cJ8f27dt18cUX/+lVBC9kf3YkT7Vq1RxK4n/Lb3Z2tubOnavvv/9eGRkZuvjii9WrVy+FhYW5Hc0Vedco+Pjjj9WwYUOf53744QfdcMMNuuOOOzRmzJiiC1GkFe0CtHHjRhMeHm5atGhh5s2bZ9avX2+Sk5PNW2+9ZS677DITHh5uNmzY4GimsLAws3v3bmPMyT1lycnJxhhjtm7dasqWLetoFpxd3t4g+MrKyjJpaWlmz549PkNJlZmZaVq2bGluvvlmk5uba3744QdToUIFM3jwYLej+Y2vvvrKJCQkWHt0886lqF+/vlm1apXjeeLj461P7i+55BLz4osvGmOM+fTTT010dLTjefzBp59+aq6++mqza9cut6MYY05uK/OynLq3cMeOHY6eQxcWFmbN+9QcycnJJjIy0rEcp8rOzjbvvvuuGTt2rBk7dqyZP3++yc7OdjzHqX/PBQ1O8rflF75+//1307JlSxMYGGj+9re/mcGDB5tBgwaZa665xgQGBporrriiyN9zUaLOARtv2JGdnW3GjBljKleubAIDA60N5mOPPVbiLxSwdetW07p163wbSjdOIvY3hw4dMk2aNDH/+Mc/TIUKFcxDDz3kdiS/tG7dOjNv3jwzb948Vw+96du3rxk1apQxxphp06aZsLAw06lTJxMVFWXuuusu13K5KSoqyoSEhJiAgAATHh5uoqOjfQanxcfHm88++8wY41teZs+eberXr+9YjjZt2pipU6daOXbu3GmMMWbAgAHmmmuucSxHnm3btpk6deqYUqVKWRdCKVWqlKlbt67Zvn27o1mSk5N9hjVr1piXX37Z1KtXz7z33nuOZvG35XfOnDmmVatWJjY21vrg/JlnnjEffPCB41n8RVZWlpkwYYJp0qSJCQsLM2FhYaZJkyYmMTGxwEPizzcO5/sLkpOTfW6227RpU1dy9OvXT3FxcXr88cf13HPP6eGHH1arVq20du1adevWTTNnznQlF04aM2aMZs+erTFjxqh///7asGGDatasqXnz5mny5Mn66quvHM2Tk5OjWbNmacmSJdq/f79yc3N9nv/iiy8cy5J36eGhQ4cWeOJukyZNHMvitoIO4fnpp5901VVX6frrr9eECROs8UV5U9Di7Pjx4zp+/LjCw8Mdn3dubq5yc3Otwy/nzZunlStXqnbt2rr77rsdvwm7P8i7cMKZ9O7d26EkJyUmJur111/XK6+8oquuukoLFy7Unj17NHjwYI0YMUIDBw50JMeKFSt07bXX6rbbbtOsWbP0r3/9SykpKVq1apWWLVumSy65xJEceTp37ixjjN544w2VLVtWknTgwAHddtttCggI0Mcff+xonoJ8/PHHeuqpp5SUlOTYPP1p+X3hhRc0cuRIDRo0SE888YQ2btyomjVratasWZo9e7aWLl3qWBb8gRJ1nrDxxpnUqlVLL730kjp27Ohz/PvmzZt1xRVX6NChQ47mGTBggGbNmqXrrruuwOIyadIkx7KULl1a3377rerVq+fYPP3V2W4EKsm6eTP38jop76bDl19+uXr16qXhw4dr4sSJys7OVocOHTR37lzFxMQ4munYsWP6/vvv83044fF4dMMNNziaBfkZYzR+/HglJiZa99Xxer166KGHNHbsWEez7Ny5U4mJiVq/fr11fssjjzyiRo0aOZpDOrke/vrrr/PNe/369WrVqpUyMjIcz3S67du3q0mTJjp69KjbUVyRkJCg8ePHWzfdzXsfsWHDBrVr106//vqr2xEdt3r1al1yySUKDAws8PmsrCwtWLBAN998c5Fl4BLn58DfNt4BAQE6fvy4vvvuO+3fv19hYWHq1KmTJGnRokVsvF22d+/eAi9JnZubqxMnTjieZ+7cuXr77bf94rKoCQkJJXLlXxA+SSy8cePGady4cWrVqpXefPNNrVixQh988IHGjBmjgIAATZ06VY899pheeOEFxzItWrRIt99+e4EXkChJxTc9Pd3aU/pnF0hweo+qx+PRo48+qocffljbt29XRkaGEhISHP/w84477lD79u01dOhQXXTRRY7OuyBer7fAS0FnZGQoJCTE0SynLzPGGP30008aNWqUateu7cj8/XH53bVrV4EXuPB6vSW2WF5xxRX66aefrMvhR0ZGKjk5WTVr1pQk/fbbb+rRowclyp+w8YZdCQkJWr58uapXr+4z/t1333Xlqj8hISF+c5+h//znP/r3v/+t8ePHq1GjRvn2mpakw9batm3rdoRiY9asWZo5c6Z69OihtWvXqkWLFnr77bd10003SZIaNmyou+++29FMAwcO1M0336yRI0ee9d4lTtm2bZuWLl1a4CG7I0eOLLL5RkdHW29soqKizrh31c1tU0hIiBISElyZd978ExMT1a9fP1WuXFlt27ZVu3bt1LZtW0eKwumuv/56/fOf/9TMmTOt+2B+8803uvvuux2/j2FBy4wxRnFxcZo7d26Rz99fl9/4+HglJyfnex+xaNEi1a9f37Ec/uT0A+kKOrCuqA+2o0TZxMb7z7m18fZXI0eOVO/evbV3717l5uZq/vz52rJli+bMmaOPPvrI8TwPPvigpkyZomnTprl+88u8PaYdOnTwyeL2myx/kZmZWeANtBs3buxSIv+Qmppq3Ri1efPmCgoK8rnEbePGjfXTTz85munnn3/WkCFD/GIdPH36dN1zzz0qV66cKlWq5PO35fF4inQ9/MUXX1jn1fjD3tVu3boVetr58+cXYZI/zJgxQ9LJoxS+/PJLLVu2TBMnTtS//vUvxcbG6scff3QkR56pU6eqd+/euuKKK6wPsk6cOKEbb7zR8dspnL7MBAQEqHz58qpVq5Z1ykJR8rflN8+QIUN033336dixYzLGaPXq1XrrrbeUmJhoLU/Ir6jf41CibGLjfXZubrz91Y033qj//ve/GjNmjEqXLq2RI0fq4osv1n//+19dddVVjmQ4/Y3EF198oU8++UQNGjTIt/fHqTcSkn9tpPzJL7/8ojvvvFOffPJJgc+X9HJ54sQJeb1e63FISIjPchwUFOT4z+gf//iHkpKS/OLwrCeeeELjxo3TI4884vi8T92j2rZtWy1fvlwvvfSSduzYoXfffVdVqlTRa6+9dtb71Z1PZcqUKdR0bnygFB0drZiYGEVHRysqKkpBQUEqX7684zmioqK0YMECbd++XSkpKZJOHkHhxhELectPSkqK9QHSoUOHrIt4FfWeMX9bfvP069dPYWFheuyxx5SZmamePXuqSpUqmjJlim699VZHs+APlCib2HifnZsb7zM5dWV8KqcOU+jdu7f69u2rzz77zJH5FeT0NxJdu3Z1KYmvtm3b6rffftPMmTO1adMmSSc33n379i30m5/zze3lRZIGDRqk3377Td98843atWun999/Xz///LOeeOIJTZw40bEc/iwlJcW6OaoxRps3b7ZOgHfjPLtp06ape/fuWr58eYGHpt5///2OZTl06JC6d+/u2PzO5L333tPtt9+uXr16ad26dcrKypIkHT58WOPHj9fChQuLPMOrr75qff3WW2+pR48eBU738MMPF3mWPMOHD1dSUpLWrVun+vXrq23btho6dKiuvPJKRUdHO5bjVDNnztSkSZO0bds2SVLt2rU1aNAg9evXz9EcO3fuVLdu3fT9999bF9OR/ii5Tr6/8oflN8/vv/+url27qlevXsrMzNSGDRu0cuVKVa1a1bEM/sj17UCRX0T9AuPxeMzSpUvN+vXrzfr1603p0qXNxx9/bD1esmSJ4/e3OXr0qOncubPp3bu3efrpp82UKVN8BidFRERY995w244dO0zjxo2tew55PB6fe3s55cYbbzTBwcGmVq1aZty4cWbv3r2OzbsgmZmZJiMjw3q8a9cuM2nSJLNo0SLHs6xZs8bExMSYKlWqmK5du5quXbuaqlWrmpiYGPPtt986msVflhdjjKlUqZL55ptvjDEn/6a2bNlijDFmwYIFplWrVo5m8Uen/45OHdy6z9iMGTNMUFCQCQ8PN9WrVzc1atSwhvj4eEez3HXXXeaFF15wdJ4Fadq0qZk9e7Yxxve+TN99952pWLGi43nKlCljFi5cmG/84MGDTaVKlRzL4fF4TIUKFUxiYqL1t+2mESNGmNKlS5uhQ4eaBQsWmAULFpihQ4ea8PBwM2LECEezXH/99ebGG280v/zyiwkPDzcbN240y5cvN5dddpn58ssvHc3iT8vvVVddZf1NHzp0yFSsWNFUrVrVhIaGmueff97RLP7CH7YDlCib/OGXdjo23gU7fWWckpLi2sp4//79ZuLEiaZx48YmKCjI/O1vfzNvv/22OX78uKM5jPGvlXHr1q1Nnz59zIkTJ6xxJ06cML179zZt2rRxNIs/LS8RERFm165dxhhjqlWrZlasWGGMMWbnzp0mLCzM0Sz+aPfu3YUanFSxYkUzbtw4k5OT4+h8CzJ+/HhTrlw51z9YCwsLs5bjU9+E7tixw3i9Xsdy5Pnoo49MmTJlzPLly61xAwYMMLGxsWbTpk2O5UhOTjZTpkwxXbt2NeXKlTOVK1c2PXr0MC+99JIrpapcuXLmzTffzDf+zTffNDExMY5miYmJMevXrzfGGBMZGWk2b95sjDFmyZIlpmnTpo5m8aflNyYmxmzYsMEYY8z06dNN48aNTU5Ojnn77bdNvXr1HM3iL/xhO0CJsskffmmnY+NdMH9aGZ/q22+/NQMGDDChoaGmXLlyZtCgQWbr1q2Ozd+fVsahoaEFvnnZuHGj42XBn5aX5s2bW3sGb7jhBnP77bebH3/80fz73/82NWvWdDSLv1m/fr2tdd2GDRt8SnpRiY6ONtu3by/y+RTGqR+knT44+cFafHy8+eyzz4wxvm9CZ8+eberXr+9YjlO98cYbJjo62qxdu9bcc889pnLlyq7vDUpOTja9e/c2QUFBjn8Ia8zJPXQFbYO2bNliypQp42iWqKgos3PnTmOMMTVr1jRffPGFMcaY7du3O75N8KflNywszOzZs8cYY0z37t3NqFGjjDHGpKamlsgP1vxlO0CJssFffmmnY+NdMH9aGef53//+ZyZMmGDq1q1rSpcube644w7TsWNHExQUZJ555hlHMvjTyrhChQrm008/zTd+0aJFpkKFCo5m8afl5bXXXjOvvvqqMcaYtWvXmnLlyhmPx2O8Xq+ZO3euo1n8TUBAgNm/f3+hp3fqEONBgwaZcePGFfl8ipPx48ebhIQE8/XXX5uIiAizfPly8/rrr5vy5cubqVOnupbrueeeM16v11StWtVs27bN8fnn5uaab7/91kycONHccMMNJjo62gQGBppmzZqZQYMGOZ5nwIABZvDgwfnGP/jgg+bee+91NEvr1q3N+++/b4wxpkePHuZvf/ubWbFihbnjjjtMgwYNHM3iT8tvo0aNzJQpU0xqaqqJjIw0q1atMsac3D64cWis2/xlO8CFJWxo1qyZ9u3bV+ir51xxxRU+N/4qKr1799a8efM0fPjwIp1PYezatcvtCJaGDRtq/fr1io+PV4sWLfTkk08qJCREL7/8cpH/Tk514sQJffjhh3r11Ve1ePFiNW7cWIMGDVLPnj2t+yC9//77uuuuuzR48OAiz1OrVi198MEH6tq1qz799FNrnvv373f8vky33HKL+vbtq6efflotW7aUJK1cuVIPP/zwGU8ALyr+srxI0m233WZ9fckll2jPnj3avHmzqlWrpnLlyjmaxd8YYzRixAiVKlWqUNOffoGQopKTk6Mnn3xSn376qRo3bpzvwhLPPPNMkc5/yJAhGjt2rEqXLq0hQ4accTqPx+PYxUmGDh2q3NxcdezYUZmZmbryyivl9Xr10EMPaeDAgY5kONPPonz58rr44ov1/PPPW+OK+neUp2zZssrIyFCTJk3Utm1b9e/fX23atFFUVJQj85d8fy4ej0czZszQ4sWLdfnll0s6eZ+o1NRU3XHHHY5lkqTHHnvMunnsmDFjdP3116tNmzaKiYnRvHnzHM3iD8tvnpEjR6pnz54aPHiwOnbsqCuuuEKStHjxYlfuN3kmnTp10s6dO7Vz584inY+/bAc8xhTxnaguIAEBAfrnP/9Z6F/a888/r5SUlCJ/A3b//fdrzpw5atKkCRvvU3z66ac6evSounXrpu3bt+v666/X1q1brZVxhw4dHMlRrlw55ebmqkePHurfv7+aNm2ab5rffvtNzZo1c6SEvvvuu+rZs6dycnLUsWNHLV68WJKUmJioL7/88oyX1S4Kx48f18MPP6wXX3xR2dnZkqTg4GDdc889mjBhgs+VMIua28vL2f52TufUm70/49QG81Tt2rWzfTnqN998U7GxsUWU6KT27duf8TmPx6MvvviiyOf//vvvKyoqyvUspzt+/Li2b9+ujIwMJSQkKDw83LF5n+1ncSonfy4ff/yx2rRp4+rNxP3x53ImBw8eVHR0tGv3NXRz+T3Vvn379NNPP6lJkyYKCAiQJK1evVqRkZGqV6+eK5lO99xzz+nXX3/V448/XqTz8ZftACXKBn/5pZ3O7Q2mP2+8T+fGyvi1115T9+7dFRoa6tg8/4y/rYwzMzO1Y8cOSdJFF11U6A8qipqTy0txelOTx6kNJgAA/oYSBQAAAAA2BLgdAAAAAACKE0oUAAAAANhAiToPsrKyNGrUKGVlZbkdhSxkIQtZSkSWU/lTLn/KIvlXHrKQhSxkKUpO5+GcqPMgPT1dZcqU0eHDh1292g5ZyEIWspSULKfyp1z+lMXf8pCFLGQhy4WUhz1RAAAAAGADJQoAAAAAbAhyO4CbcnNz9b///U8RERF/6T4w6enpPv+6iSwFI0vByFIwshTsfGUxxujIkSOqXLmyJP3l9fCF+DM6X/wpD1kKRpaCkaVgZDmz85Hn1O1T3n00z6REnxP1448/Ki4uzu0YAFAipaWlSRLrYQCAX0lLS1PVqlXPOk2J3hMVEREhSdrzXQ1Fhrt/ZGPXOo3cjgAARS5bJ7RCC611sCQ16DFCgSGhLqY6admjM92OYOm+/Sq3I1gOZpZyO4KPYbU+cTuCZdgPXdyOYCn1ecSfT+SQ0EO5bkeweH874XYES8iGVLcj+PCEet2OYDFl3F9+s3OytGzrsz7bpzMp0SUq79CRyPAARUa4X6KCPMFuRwCAovf/xz+cevheYEioX5Qof9gW5AkuHeJ2BEugx3/eaElSqYhAtyNYAku5v9zm8Ye/oTxBwf5TooKC/Gd5CQrwn79rSfIE+M/ftgn0nyyFObzcf7YWAAAAAFAMUKIAAAAAwAZKFAAAAADYQIkCAAAAABsoUQAAAABgAyUKAAAAAGygRAEAAACADZQoAAAAALCBEgUAAAAANlCiAAAAAMAGShQAAAAA2ECJAgAAAAAbHClR7dq108CBAzVo0CBFR0erYsWKmj59uo4ePao777xTERERqlWrlj755BNJUk5Ojvr27av4+HiFhYWpbt26mjJlis9r9unTR126dNHTTz+t2NhYxcTE6L777tOJEyec+JYAAAAAlFCO7YmaPXu2ypUrp9WrV2vgwIG655571L17d7Vs2VLfffedrr76at1+++3KzMxUbm6uqlatqnfeeUcpKSkaOXKkhg8frrffftvnNZcuXaodO3Zo6dKlmj17tmbNmqVZs2Y59S0BAAAAKIEcK1FNmjTRY489ptq1a2vYsGEKDQ1VuXLl1L9/f9WuXVsjR47UgQMH9P333ys4OFijR49W8+bNFR8fr169eunOO+/MV6Kio6M1bdo01atXT9dff72uu+46LVmy5IwZsrKylJ6e7jMAAJyTa3KUrZNHDLAeBgAUV46VqMaNG1tfBwYGKiYmRo0aNbLGVaxYUZK0f/9+SdJzzz2nSy65ROXLl1d4eLhefvllpaam+rxmgwYNFBgYaD2OjY21/n9BEhMTVaZMGWuIi4s7L98bAKBwdmmzVmihJCkuLo71MACgWHKsRAUHB/s89ng8PuM8Ho8kKTc3V3PnztVDDz2kvn37avHixUpOTtadd96p48eP/+lr5ubmnjHDsGHDdPjwYWtIS0v7q98WAMCGeNVTa3WWJKWlpbEeBgAUS0FuByjIypUr1bJlS917773WuB07dvzl1/V6vfJ6vX/5dQAA5ybAE6ggc/IDsMjISJfTAABwbvzyEue1a9fW2rVr9emnn2rr1q0aMWKE1qxZ43YsAAAAAPDPEvWvf/1L3bp10y233KIWLVrowIEDPnulAAAAAMAtjhzOl5SUlG/c7t27840zxlhfv/rqq3r11Vd9nk9MTLS+LuhS5pMnTz7XiAAAAABQKH65JwoAAAAA/BUlCgAAAABsoEQBAAAAgA2UKAAAAACwgRIFAAAAADZQogAAAADABkoUAAAAANhAiQIAAAAAGyhRAAAAAGADJQoAAAAAbAhyOwAAAMcjPAr0etyOoSd+red2BMv2/eXcjmCJLH3M7Qg+pqZ1dDuC5fdDYW5HsISEuf83lCeznP98Tn8iLMTtCJao32LdjuAjt1Sw2xEs2aXdryXZ2cHSpsJN635aP9Dp0bsUGBzqdgyVX7bb7QiWrLb73I4AAAAA+CX/+ZgAAAAAAIoBShQAAAAA2ECJAgAAAAAbKFEAAAAAYAMlCgAAAABsoEQBAAAAgA2UKAAAAACwgRIFAAAAADZQogAAAADABkoUAAAAANhAiQIAAAAAG4pViWrXrp0GDRrkdgwAAAAAJViQ2wHsmD9/voKDg92OAQAAAKAEK1YlqmzZsm5HAAAAAFDCFdvD+WrUqKHx48frrrvuUkREhKpVq6aXX37Z3YAAAAAALnjFqkSdbuLEiWrevLnWrVune++9V/fcc4+2bNnidiwAAAAAF7BiXaI6d+6se++9V7Vq1dIjjzyicuXKaenSpWecPisrS+np6T4DAMA5uSZH2TohSayHAQDFVrEuUY0bN7a+9ng8qlSpkvbv33/G6RMTE1WmTBlriIuLcyImAOD/7dJmrdBCSVJcXBzrYQBAsVSsS9TpV+rzeDzKzc094/TDhg3T4cOHrSEtLa2oIwIAThGvemqtzpKktLQ01sMAgGKpWF2d76/yer3yer1uxwCAEivAE6ggc/IDsMjISJfTAABwbor1nigAAAAAcBolCgAAAABsKFaH8yUlJVlf7969O9/zycnJjmUBAAAAUDKxJwoAAAAAbKBEAQAAAIANlCgAAAAAsIESBQAAAAA2UKIAAAAAwAZKFAAAAADYQIkCAAAAABsoUQAAAABgAyUKAAAAAGygRAEAAACADUFuBwAAICTDKPC4cTuGWpfe6nYEy+FaYW5HsKzaH+92BB+PVv/I7QiWT6KbuB3Bsnhla7cjWEr9ku12BEvIbyfcjmAx6za6HcFHUHS02xEsweGl3Y6g7NysQk9LiZIU9ssJBQUFuh1DGWOruB3Bsuf1WLcjWGrdts7tCAAAAICFw/kAAAAAwAZKFAAAAADYQIkCAAAAABsoUQAAAABgAyUKAAAAAGygRAEAAACADZQoAAAAALCBEgUAAAAANlCiAAAAAMAGShQAAAAA2ECJAgAAAAAbKFEAAAAAYAMlCgAAAABsoEQBAAAAgA3FvkQtWrRIrVu3VlRUlGJiYnT99ddrx44dbscCAAAAcIEq9iXq6NGjGjJkiNauXaslS5YoICBAXbt2VW5urtvRAAAAAFyAgtwO8FfddNNNPo9feeUVlS9fXikpKWrYsKHPc1lZWcrKyrIep6enO5IRAHBSrslRtk5IYh0MACi+iv2eqG3btqlHjx6qWbOmIiMjVaNGDUlSampqvmkTExNVpkwZa4iLi3M4LQCUbLu0WSu0UJIUFxfHehgAUCwV+xJ1ww036ODBg5o+fbq++eYbffPNN5Kk48eP55t22LBhOnz4sDWkpaU5HRcASrR41VNrdZYkpaWlsR4GABRLxfpwvgMHDmjLli2aPn262rRpI0lasWLFGaf3er3yer1OxQMAnCbAE6ggEyxJioyMdDkNAADnpliXqOjoaMXExOjll19WbGysUlNTNXToULdjAQAAALiAFevD+QICAjR37lx9++23atiwoQYPHqynnnrK7VgAAAAALmDFek+UJHXq1EkpKSk+44wxLqUBAAAAcKEr1nuiAAAAAMBplCgAAAAAsIESBQAAAAA2UKIAAAAAwAZKFAAAAADYQIkCAAAAABsoUQAAAABgAyUKAAAAAGygRAEAAACADZQoAAAAALCBEgUAAAAANgS5HcAfeH8+qqDAbLdj6FiVCLcjWAL+F+p2BItp1dTtCBbPymS3IwAXpNwgyeMHW6T5hy5xO4LFG+D+dinPgfTSbkfwse14JbcjWNKz/Wd7mesHf0N5TpQOdDuCJeCEcTuCJbRiBbcj+PCULuV2BIsJ87odQSan8H9E7IkCAAAAABsoUQAAAABgAyUKAAAAAGygRAEAAACADZQoAAAAALCBEgUAAAAANlCiAAAAAMAGShQAAAAA2ECJAgAAAAAbKFEAAAAAYAMlCgAAAABsoEQBAAAAgA2OliiPx6MPPvhAkrR79255PB4lJyc7GQEAAAAA/hL2RAEAAACADZQoAAAAALCh0CVqzpw5iomJUVZWls/4Ll266Pbbb5ckLViwQBdffLFCQ0NVs2ZNjR49WtnZ2YUOs2zZMl122WXyer2KjY3V0KFDrf//0UcfKSoqSjk5OZKk5ORkeTweDR061Pr//fr102233Vbo+QEAAACAXYUuUd27d1dOTo4+/PBDa9z+/fv18ccf66677tLy5ct1xx136IEHHlBKSopeeuklzZo1S+PGjSvU6+/du1edO3fWpZdeqvXr1+uFF17QzJkz9cQTT0iS2rRpoyNHjmjdunWSThaucuXKKSkpyXqNZcuWqV27dmecR1ZWltLT030GAIBzck2OsnVCklgPAwCKrUKXqLCwMPXs2VOvvvqqNe71119XtWrV1K5dO40ePVpDhw5V7969VbNmTV111VUaO3asXnrppUK9/vPPP6+4uDhNmzZN9erVU5cuXTR69GhNnDhRubm5KlOmjJo2bWqVpqSkJA0ePFjr1q1TRkaG9u7dq+3bt6tt27ZnnEdiYqLKlCljDXFxcYX99gEA58EubdYKLZQkxcXFsR4GABRLts6J6t+/vxYvXqy9e/dKkmbNmqU+ffrI4/Fo/fr1GjNmjMLDw62hf//++umnn5SZmfmnr71p0yZdccUV8ng81rhWrVopIyNDP/74oySpbdu2SkpKkjFGy5cvV7du3VS/fn2tWLFCy5YtU+XKlVW7du0zzmPYsGE6fPiwNaSlpdn59gEAf1G86qm1OkuS0tLSWA8DAIqlIDsTN2vWTE2aNNGcOXN09dVXa+PGjfr4448lSRkZGRo9erS6deuW7/+Fhoael7Dt2rXTK6+8ovXr1ys4OFj16tVTu3btlJSUpEOHDp11L5Qkeb1eeb3e85IFAGBfgCdQQSZYkhQZGelyGgAAzo2tEiWdvHjD5MmTtXfvXnXq1Mk6FOPiiy/Wli1bVKtWrXMKUr9+fb333nsyxlh7o1auXKmIiAhVrVpV0h/nRU2aNMkqTO3atdOECRN06NAhPfjgg+c0bwAAAAAoLNuXOO/Zs6d+/PFHTZ8+XXfddZc1fuTIkZozZ45Gjx6tjRs3atOmTZo7d64ee+yxQr3uvffeq7S0NA0cOFCbN2/WggUL9Pjjj2vIkCEKCDgZMzo6Wo0bN9Ybb7xhXUDiyiuv1HfffaetW7f+6Z4oAAAAAPirbJeoMmXK6KabblJ4eLi6dOlijb/mmmv00UcfafHixbr00kt1+eWXa9KkSapevXqhXrdKlSpauHChVq9erSZNmujuu+9W375985Wwtm3bKicnxypRZcuWVUJCgipVqqS6deva/XYAAAAAwBbbh/NJJy9H3qtXr3znF11zzTW65pprzvj/jDHW1zVq1PB5LJ0sSKtXrz7rvCdPnqzJkyf7jEtOTi5ccAAAAAD4i2yVqEOHDikpKUlJSUl6/vnniyoTAAAAAPgt21fnO3TokP7zn/9w6BwAAACAEslWidq9e3cRxQAAAACA4sH2hSUAAAAAoCSjRAEAAACADZQoAAAAALCBEgUAAAAANlCiAAAAAMAGShQAAAAA2ECJAgAAAAAbbN0n6kJlfvxJxhPidgyF7vWfThsdm+B2BIsJ8LgdwfLjYy3djmCJe2KV2xGA8yY71CPjdf9vfffRGLcjWI6ecH+7lCc31/3fzane2nuZ2xEs+45EuB3hD+H+83sKzHI7wR9yY/zn7W5IXAW3I/jICQt2O4Ilu1Sg2xGUnR0sbSrctP7zrh0AAAAAigFKFAAAAADYQIkCAAAAABsoUQAAAABgAyUKAAAAAGygRAEAAACADZQoAAAAALCBEgUAAAAANlCiAAAAAMAGShQAAAAA2ECJAgAAAAAbKFEAAAAAYIOrJapPnz7q0qXLWaepUaOGJk+e7EgeAAAAAPgzQW4H+DNr1qxR6dKlzzpNUlKS2rdvr0OHDikqKsqZYAAAAABKJL8vUeXLlz/r8ydOnHAoCQAAAAA4dDjfu+++q0aNGiksLEwxMTHq1KmTjh49aj3/9NNPKzY2VjExMbrvvvt8itHph/N5PB698MIL+vvf/67SpUurf//+at++vSQpOjpaHo9Hffr0ceLbAgAAAFACFfmeqJ9++kk9evTQk08+qa5du+rIkSNavny5jDGSpKVLlyo2NlZLly7V9u3bdcstt6hp06bq37//GV9z1KhRmjBhgiZPnqzAwED9/e9/10033aQtW7YoMjJSYWFhBf6/rKwsZWVlWY/T09PP7zcLADirXJOjbJ38oIx1MACguHKkRGVnZ6tbt26qXr26JKlRo0bW89HR0Zo2bZoCAwNVr149XXfddVqyZMlZS1TPnj115513Wo937dolSapQocJZz4lKTEzU6NGj/+J3BAA4V7u0Wbu0SZIUFxfnchoAAM5NkR/O16RJE3Xs2FGNGjVS9+7dNX36dB06dMh6vkGDBgoMDLQex8bGav/+/Wd9zebNm59TlmHDhunw4cPWkJaWdk6vAwA4N/Gqp9bqLElKS0tjPQwAKJaKvEQFBgbqs88+0yeffKKEhAQ9++yzqlu3rrX3KDg42Gd6j8ej3Nzcs77mn12t70y8Xq8iIyN9BgCAcwI8gQrSyfU+62EAQHHlyIUlPB6PWrVqpdGjR2vdunUKCQnR+++/f95ePyQkRJKUk5Nz3l4TAAAAAApS5CXqm2++0fjx47V27VqlpqZq/vz5+uWXX1S/fv3zNo/q1avL4/Hoo48+0i+//KKMjIzz9toAAAAAcKoiL1GRkZH68ssv1blzZ9WpU0ePPfaYJk6cqGuvvfa8zaNKlSoaPXq0hg4dqooVK2rAgAHn7bUBAAAA4FRFfnW++vXra9GiRQU+N2vWrHzjTr0nlCTt3r3b53HepdFPN2LECI0YMeJcIgIAAABAoTlyThQAAAAAXCgoUQAAAABgAyUKAAAAAGygRAEAAACADZQoAAAAALCBEgUAAAAANlCiAAAAAMAGShQAAAAA2ECJAgAAAAAbKFEAAAAAYEOQ2wH8QUBMtAICvG7H0InK0W5HsJRbuc/tCBZPRqbbESzVMsu5HcHy49CWbkfwUWXCKrcjoBjLCZXk/mpYvx0LczuCJe3HGLcjWAJCctyO4GPrtspuR7AEpQe6HcESFOl2gj/kBnncjmAJznA7wR+OVivtdgQfOSH+83vK9YM/pZzjha9G7IkCAAAAABsoUQAAAABgAyUKAAAAAGygRAEAAACADZQoAAAAALCBEgUAAAAANlCiAAAAAMAGShQAAAAA2ECJAgAAAAAbKFEAAAAAYAMlCgAAAABsoEQBAAAAgA2UKAAAAACwgRIFAAAAADZQogAAAADABr8uUVlZWbr//vtVoUIFhYaGqnXr1lqzZo0kKSkpSR6PR0uWLFHz5s1VqlQptWzZUlu2bHE5NQAAAIALmV+XqH//+9967733NHv2bH333XeqVauWrrnmGh08eNCa5tFHH9XEiRO1du1aBQUF6a677jrj62VlZSk9Pd1nAAA4J9fkKFsnJIn1MACg2PLbEnX06FG98MILeuqpp3TttdcqISFB06dPV1hYmGbOnGlNN27cOLVt21YJCQkaOnSoVq1apWPHjhX4momJiSpTpow1xMXFOfXtAAAk7dJmrdBCSVJcXBzrYQBAseS3JWrHjh06ceKEWrVqZY0LDg7WZZddpk2bNlnjGjdubH0dGxsrSdq/f3+Brzls2DAdPnzYGtLS0oooPQCgIPGqp9bqLElKS0tjPQwAKJaC3A7wVwUHB1tfezweSVJubm6B03q9Xnm9XkdyAQDyC/AEKsicXG9HRka6nAYAgHPjt3uiLrroIoWEhGjlypXWuBMnTmjNmjVKSEhwMRkAAACAksxv90SVLl1a99xzjx5++GGVLVtW1apV05NPPqnMzEz17dtX69evdzsiAAAAgBLIb0uUJE2YMEG5ubm6/fbbdeTIETVv3lyffvqpoqOj3Y4GAAAAoITy6xIVGhqqqVOnaurUqfmea9eunYwxPuOaNm2abxwAAAAAnE9+e04UAAAAAPgjShQAAAAA2ECJAgAAAAAbKFEAAAAAYAMlCgAAAABsoEQBAAAAgA2UKAAAAACwgRIFAAAAADZQogAAAADABkoUAAAAANgQ5HYAAAByvEYKNW7H0N6NFd2OYCn1i/98zpkd7v7v5lSBxzxuR7Bkh/nPzybHj7LI4z+/oxyv/2TJig50O4IPjx8tMsYPfk05WYVf71KiJJmwUJlAr9sxFLz3oNsRLCbIf/7IcyuWdTuCJfDAEbcjWKp9eMLtCD7297vC7QiWmBlfuR0BAABcwPznYy4AAAAAKAYoUQAAAABgAyUKAAAAAGygRAEAAACADZQoAAAAALCBEgUAAAAANlCiAAAAAMAGShQAAAAA2ECJAgAAAAAbKFEAAAAAYAMlCgAAAABsoEQBAAAAgA2UKAAAAACwgRIFAAAAADYU+xK1aNEitW7dWlFRUYqJidH111+vHTt2uB0LAAAAwAWq2Jeoo0ePasiQIVq7dq2WLFmigIAAde3aVbm5ufmmzcrKUnp6us8AAHBOrslRtk5IEuthAECxFeR2gL/qpptu8nn8yiuvqHz58kpJSVHDhg19nktMTNTo0aOdjAcAOMUubdYubZIkxcXFuZwGAIBzU+z3RG3btk09evRQzZo1FRkZqRo1akiSUlNT8007bNgwHT582BrS0tIcTgsAJVu86qm1OkuS0tLSWA8DAIqlYr8n6oYbblD16tU1ffp0Va5cWbm5uWrYsKGOHz+eb1qv1yuv1+tCSgCAJAV4AhVkgiVJkZGRLqcBAODcFOsSdeDAAW3ZskXTp09XmzZtJEkrVqxwORUAAACAC1mxLlHR0dGKiYnRyy+/rNjYWKWmpmro0KFuxwIAAABwASvW50QFBARo7ty5+vbbb9WwYUMNHjxYTz31lNuxAAAAAFzAivWeKEnq1KmTUlJSfMYZY1xKAwAAAOBCV6z3RAEAAACA0yhRAAAAAGADJQoAAAAAbKBEAQAAAIANlCgAAAAAsIESBQAAAAA2UKIAAAAAwAZKFAAAAADYQIkCAAAAABsoUQAAAABgQ5DbAQAAyPVK8rqdQqoz4ge3I1hOXFrX7QiW3BD/+sw1bOP/3I5gOVG9vNsRLLtvKOV2BEt23Uy3I1giI/wnywO1v3A7go/vjtZwO4Il9Wi02xF04uhxbZtYuGkpUZJyt+9RrifY7RhSgMftBH/INW4nsJjsE25HsARUreJ2hD9k57idwMfBK7PcjmCJ2dDY7Qh/+Pp7txMAAIDzzL8+WgIAAAAAP0eJAgAAAAAbKFEAAAAAYAMlCgAAAABsoEQBAAAAgA2UKAAAAACwgRIFAAAAADZQogAAAADABkoUAAAAANhAiQIAAAAAGyhRAAAAAGCDoyWqXbt2GjRoUKGmTUpKksfj0W+//VakmQAAAADADvZEAQAAAIANlCgAAAAAsMG1EvXaa6+pefPmioiIUKVKldSzZ0/t37//jNNnZmbq2muvVatWraxD/GbMmKH69esrNDRU9erV0/PPP+9QegAAAAAlVZBbMz5x4oTGjh2runXrav/+/RoyZIj69OmjhQsX5pv2t99+03XXXafw8HB99tlnKlWqlN544w2NHDlS06ZNU7NmzbRu3Tr1799fpUuXVu/evQucZ1ZWlrKysqzH6enpRfb9AQDyyzU5ytYJSayDAQDFl2sl6q677rK+rlmzpqZOnapLL71UGRkZCg8Pt57bt2+fbrnlFtWuXVtvvvmmQkJCJEmPP/64Jk6cqG7dukmS4uPjlZKSopdeeumMJSoxMVGjR48uwu8KAHA2u7RZu7RJkhQXF+dyGgAAzo1rh/N9++23uuGGG1StWjVFRESobdu2kqTU1FSf6a666irVqlVL8+bNswrU0aNHtWPHDvXt21fh4eHW8MQTT2jHjh1nnOewYcN0+PBha0hLSyu6bxAAkE+86qm1OkuS0tLSWA8DAIolV/ZEHT16VNdcc42uueYavfHGGypfvrxSU1N1zTXX6Pjx4z7TXnfddXrvvfeUkpKiRo0aSZIyMjIkSdOnT1eLFi18pg8MDDzjfL1er7xe73n+bgAAhRXgCVSQCZYkRUZGupwGAIBz40qJ2rx5sw4cOKAJEyZYh3OsXbu2wGknTJig8PBwdezYUUlJSUpISFDFihVVuXJl7dy5U7169XIyOgAAAIASzpUSVa1aNYWEhOjZZ5/V3XffrQ0bNmjs2LFnnP7pp59WTk6OOnTooKSkJNWrV0+jR4/W/fffrzJlyuhvf/ubsrKytHbtWh06dEhDhgxx8LsBAAAAUJK4ck5U+fLlNWvWLL3zzjtKSEjQhAkT9PTTT5/1/0yaNEk333yzOnTooK1bt6pfv36aMWOGXn31VTVq1Eht27bVrFmzFB8f79B3AQAAAKAkcnRPVFJSkvV1jx491KNHD5/njTHW1+3atfN5LElTp07V1KlTrcc9e/ZUz549iyYsAAAAABTAtavzAQAAAEBxRIkCAAAAABsoUQAAAABgAyUKAAAAAGygRAEAAACADZQoAAAAALCBEgUAAAAANlCiAAAAAMAGShQAAAAA2ECJAgAAAAAbKFEAAAAAYEOQ2wH8QoBH8njcTqGAsFC3I1jMiWy3I/whwP3fjcUYtxNYPP70O5JU+cNgtyNYAg8edjuCZX//K9yOYImZ/pXbEc4oMNOjwFz3/9Y9Naq6HcFi/Gjddzwi0O0IPkJLh7kdwRJwItftCH/wn0VG2Rn+s004rFJuR7DsyqrgdgQf3gD/eS8RHpzldgSdCD5e6GnZEwUAAAAANlCiAAAAAMAGShQAAAAA2ECJAgAAAAAbKFEAAAAAYAMlCgAAAABsoEQBAAAAgA2UKAAAAACwgRIFAAAAADZQogAAAADABkoUAAAAANhAiQIAAAAAGyhRAAAAAGADJQoAAAAAbKBEAQAAAIANQW4HcFJWVpaysrKsx+np6S6mAYCSJ9fkKFsnJLEOBgAUXyVqT1RiYqLKlCljDXFxcW5HAoASZZc2a4UWSpLi4uJYDwMAiqUSVaKGDRumw4cPW0NaWprbkQCgRIlXPbVWZ0lSWloa62EAQLFUog7n83q98nq9bscAgBIrwBOoIBMsSYqMjHQ5DQAA56ZE7YkCAAAAgL/qgitR06ZNU8eOHd2OAQAAAOACdcGVqF9//VU7duxwOwYAAACAC9QFV6JGjRql3bt3ux0DAAAAwAXqgitRAAAAAFCUKFEAAAAAYAMlCgAAAABsoEQBAAAAgA2UKAAAAACwgRIFAAAAADZQogAAAADABkoUAAAAANhAiQIAAAAAGyhRAAAAAGADJQoAAAAAbAhyO4A/8IQEy+MJcTuG5PW6ncBifj/mdgSLJzDQ7QgWk5HhdgS/Fb7gJ7cjWHIDPG5HsFT48IjbESw/LajndgRJUk5mlnTraSM9/z+4bEv/aLcjWAJ/94MfiJ/6pWkFtyNYPLn+83sKPux2gj8EZQS7HeEPxn+yvPdtO7cj+Mjxg7e/eYwfvN3LySr8+1/2RAEAAACADZQoAAAAALCBEgUAAAAANlCiAAAAAMAGShQAAAAA2ECJAgAAAAAbKFEAAAAAYAMlCgAAAABsoEQBAAAAgA2UKAAAAACwgRIFAAAAADb85RLVrl07eTweeTweJScnn4dIxTMDAAAAgJLhvOyJ6t+/v3766Sc1bNhQu3fvtgrN6cPXX39t/Z/ff/9djz/+uOrUqSOv16ty5cqpe/fu2rhxo89rZ2ZmatiwYbrooosUGhqq8uXLq23btlqwYIE1zfz587V69erz8a0AAAAAwFkFnY8XKVWqlCpVquQz7vPPP1eDBg18xsXExEiSsrKy1KlTJ6WmpmrixIlq0aKFfv75ZyUmJqpFixb6/PPPdfnll0uS7r77bn3zzTd69tlnlZCQoAMHDmjVqlU6cOCA9bply5ZVenr6+fhWAAAAAOCszkuJKkhMTEy+YpVn8uTJ+uqrr7Ru3To1adJEklS9enW99957atGihfr27asNGzbI4/Howw8/1JQpU9S5c2dJUo0aNXTJJZcUVWwAAAAAOCtXLizx5ptv6qqrrrIKlBUmIECDBw9WSkqK1q9fL0mqVKmSFi5cqCNHjvzl+WZlZSk9Pd1nAAAAAAA7iqxEtWzZUuHh4T5Dnq1bt6p+/foF/r+88Vu3bpUkvfzyy1q1apViYmJ06aWXavDgwVq5cuU5ZUpMTFSZMmWsIS4u7pxeBwBwbnJPZCsnM0uS+DALAFBsFVmJmjdvnpKTk32GUxljCvU6V155pXbu3KklS5boH//4hzZu3Kg2bdpo7NixtjMNGzZMhw8ftoa0tDTbrwEAOHf731mpzXdNlSTFxcXxYRYAoFgqsnOi4uLiVKtWrQKfq1OnjjZt2lTgc3nj69SpY40LDg5WmzZt1KZNGz3yyCN64oknNGbMGD3yyCMKCQkpdCav1yuv12vjuwAAnE8VurdS2aubafNdU60PsihSAIDixpVzom699VZ9/vnn1nlPeXJzczVp0iQlJCTkO1/qVAkJCcrOztaxY8eKOioA4DwKCA5SYKmTH2ZFRkYqMjLS5UQAANhXZHuiDhw4oH379vmMi4qKUmhoqAYPHqwFCxbohhtu8LnE+fjx47Vp0yZ9/vnn8ng8kk7eSLdHjx5q3ry5YmJilJKSouHDh6t9+/ZsfAEAAAA4rshKVKdOnfKNe+utt3TrrbcqNDRUX3zxhcaPH6/hw4drz549ioiIUPv27fX111+rYcOG1v+55pprNHv2bA0fPlyZmZmqXLmyrr/+eo0cObKoogMAAADAGZ33ElWjRo1CXTSiVKlSeuKJJ/TEE0+cdbphw4Zp2LBh5yseAAAAAPwl5+WcqOeff17h4eH64YcfzsfL2XbttdeqQYMGrswbAAAAQMnyl/dEvfHGG/r9998lSdWqVfvLgc7FjBkzXM8AAAAAoGT4yyWqSpUq5yNHsc8AAAAAoGRw5RLnAAAAAFBcUaIAAAAAwAZKFAAAAADYQIkCAAAAABsoUQAAAABgAyUKAAAAAGygRAEAAACADX/5PlHFmTFGkpRtTric5CRPbqDbESy5fvIzkSSPyXE7gsXz/8sM8vOnZUbG43YCS0DucbcjWHIys9yOIOmPHOaUv6fcrGNuxfGReyzX7QgWzzH/WY79jSfb7QSnyPWj35N//IlLkoz/vKWR/GjT7fGj35Ek5fjRz8Yflpm8bZEpxPs9jynMVBeoH3/8UXFxcW7HAIASKS0tTZJYDwMA/EpaWpqqVq161mlKdInKzc3V//73P0VERMjjOfdPktLT0xUXF6e0tDRFRkaex4RkIQtZyHLhZTHG6MiRI6pcubIk/eX18IX4M7oQ85CFLGQhi7/nOXX7FBBw9rOeSvThfAEBAX/aMu2IjIz0i4VIIsuZkKVgZCkYWQp2PrKUKVPG+vp8rYcvtJ/R+eRPechSMLIUjCwFI8uZ/dU8p26fzoYLSwAAAACADZQoAAAAALCBEnUeeL1ePf744/J6vW5HIQtZyEKWEpHlVP6Uy5+ySP6VhyxkIQtZipLTeUr0hSUAAAAAwC72RAEAAACADZQoAAAAALCBEgUAAAAANlCiAAAAAMAGShQAAAAA2ECJAgAAAAAbKFEAAAAAYAMlCgAAAABs+D/qWeJIJMgw8QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['man in a yellow shirt on a lake.']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_list = [\n",
    "    \"Mann in einem kleinen weißen Boot auf einem See.\",  # Man in a small white boat on a lake.\n",
    "    # \"Ein Mann mit einem Eimer und ein Mädchen mit einem Hut am Strand.\", # A man with a bucket and a girl in a hat on the beach.\n",
    "    # \"Drei Männer auf Pferden während eines Rennens.\",  # Three men on horses during a race.\n",
    "    # \"Ein Mann und eine Frau essen zu Abend\",  # 一个男人和一个女人在吃晚餐\n",
    "]\n",
    "\n",
    "# load checkpoints\n",
    "model = TransformerModel(config)\n",
    "model.load_state_dict(state_dict)\n",
    "translator = Translator(model.cpu(), tokenizer, tokenizer)\n",
    "translator(\n",
    "    sentence_list,\n",
    "    layer_idx=-1,\n",
    "    # heads_list=[0, 1, 2, 3, 4, 5, 6, 7]\n",
    "    )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
